Evaluating the “Good Death” Concept from Iranian Bereaved Family

Editor-in-Chief 

Michael J. Fisch, MD, MPH, FACP 

Associate Editors 

David Cella, PhD 

Thomas Strouse, MD 

Debra Barton, PhD, RN, AOCN, FAAN 

March/April 2011 Volume 9 � Number 2 

Commentary 

Holistic Quarterbacking and the 80/20 Rule 

Thomas Strouse 

Review 

Personality Disorders in the Oncology Setting 

Fremonta Meyer and Susan Block 

PEER VIEWPOINTS 

Personality Disorders in the Clinical Setting 

Walter F. Baile 

Challenging Personalities in the Oncology Setting 

Matthew Doolittle and Jimmie C. Holland 

How We Do It 

Recognizing Depression in Patients with Cancer 

Alicia Morgans and Lidia Schapira 

Original Research 

Evaluating the “Good Death” Concept from 

Iranian Bereaved Family Members’ Perspective 

Sedigheh Iranmanesh et al 

Symptom Experience in Patients with 

Gynecological Cancers: The Development of 

Symptom Clusters through Patient Narratives 

Violeta Lopez et al 

Estimating Minimally Important Differences for 

the Worst Pain Rating of the Brief Pain 

Inventory–Short Form 

Susan D. Mathias et al 

A Pilot Trial of Decision Aids to Give Truthful 

Prognostic and Treatment Information to 

Chemotherapy Patients with Advanced Cancer 

Thomas J. Smith et al

The Journal of Supportive Oncology March/April 2011 

Volume 9, Number 2 (pp 43–86)

Michael J. Fisch 

David Cella 

Thomas Strouse 

Debra Barton 

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VOLUME 9, NUMBER 2 � MARCH/APRIL 2011 www.SupportiveOncology.net i

On the cover Distress is a common 

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CONTENTS March/April 2011 

The Journal of Supportive Oncology is indexed by Index Medicus/MEDLINE/PubMed, EMBASE/Excerpta Medica, 

Chemical Abstracts, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) 

COMMENTARY 

43 Holistic Quarterbacking and the 80/20 Rule 

Thomas Strouse, MD, Maddie Katz Endowed Chair in Palliative Care Research and Education, Medical 

Director, Stewart and Lynda Resnick Neuropsychiatric Hospital at UCLA, Department of Psychiatry and 

Biobehavioral Sciences, David Geffen-UCLA School of Medicine, Los Angeles, California. 

REVIEW 

44 Personality Disorders in the Oncology Setting 

Fremonta Meyer, MD, and Susan Block, MD, Department of Psychosocial Oncology and Palliative Care, 

Dana-Farber Cancer Institute, Boston, Massachusetts; Department of Psychiatry, Brigham and Women’s 

Hospital, Boston, Massachusetts; and Harvard Medical School, Boston, Massachusetts. 

Peer Viewpoints 

52 Personality Disorders in the Clinical Setting 

Walter F. Baile, MD, Program in Interpersonal Communication and Relationship Enhancement, University 

of Texas MD Anderson Cancer Center, Houston, Texas. 

53 Challenging Personalities in the Oncology Setting 

Matthew Doolittle, MD, and Jimmie C. Holland, MD, Memorial Sloan-Kettering Cancer Center, New 

York, New York. 

HOW WE DO IT 

54 Recognizing Depression in Patients with Cancer 

Alicia Morgans, MD, and Lidia Schapira, MD, Massachusetts General Hospital Cancer Center, Boston, 

Massachusetts. 

For Your Patients. 

Use your smartphone to read this QR code and link to a patient education article on depression and 

cancer. You can download a mobile barcode reader from your smartphone’s app store to go directly 

to the article. Also, you can access and download the article at http://www.supportiveoncology.net/ 

patient/0901.pdf 

ii www.SupportiveOncology.net THE JOURNAL OF SUPPORTIVE ONCOLOGY

Mission 

The mission of The Journal of Supportive 

Oncology is the publication 

of articles and original research 

relating to practical management 

issues in the palliative and 

supportive care of patients with 

neoplastic diseases. The articles 

aim to be of broad relevance to 

the clinical problems faced by the 

practicing oncologist, bringing 

research findings from bench to 

bedside. The journal focuses on 

the integration of supportive and 

palliative oncology into the care 

of all oncology patients, from 

diagnosis to cure and beyond 

or death. 

Disclaimer 

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ORIGINAL RESEARCH 

59 Evaluating the “Good Death” Concept from Iranian Bereaved Family 

Members’ Perspective 

Sedigheh Iranmanesh, PhD, Habibollah Hosseini, doctoral student, and 

Mohammad Esmaili, MSc student, Razi Faculty of Nursing and Midwifery, Kerman Medical University, 

Kerman, Iran. 

64 Symptom Experience in Patients with Gynecological Cancers: The 

Development of Symptom Clusters through Patient Narratives 

Violeta Lopez, RN, PhD, Gina Copp, RN, PhD, Lisa Brunton, RN, MSN, and 

Alexander Molassiotis, RN, PhD, Research Centre for Nursing and Midwifery Practice, Australian National 

University, Medical School, Canberra, Australia; School of Health and Social Sciences, Middlesex University, 

London; School of Nursing, Midwifery and Social Work, University of Manchester, Manchester, United 

Kingdom. 

72 Estimating Minimally Important Differences for the Worst Pain 

Rating of the Brief Pain Inventory–Short Form 

Susan D. Mathias, MPH, Ross D. Crosby, PhD, Yi Qian, PhD, Qi Jiang, PhD, Roger Dansey, MD, and 

Karen Chung, PharmD, MS, From Health Outcomes Solutions, Winter Park, Florida; Biomedical Statistics and 

Methodology, Neuropsychiatric Research Institute, Fargo, North Dakota; Global Biostatistics, Global 

Development, and Global Health Economics, Amgen, Inc., Thousand Oaks, California. 

79 A Pilot Trial of Decision Aids to Give Truthful Prognostic and 

Treatment Information to Chemotherapy Patients with Advanced 

Cancer 

Thomas J. Smith, MD, Lindsay A. Dow, MD, Enid A. Virago, MDiv, James Khatcheressian, MD, 

Robin Matsuyama, PhD, and Laurel J. Lyckholm, MD, Massey Cancer Center of Virginia Commonwealth 

University, School of Education, VCU School of Medicine, Department of Social and Behavioral Health, and the 

Virginia Cancer Institute, Richmond, Virginia. 

Available on www.SupportiveOncology.net 

GUIDE FOR AUTHORS 

Our Guide for Authors and submission process has changed. 

Please visit our website www.SupportiveOncology.net and click on Guide for Authors 

for further information. 

VOLUME 9, NUMBER 2 � MARCH/APRIL 2011 www.SupportiveOncology.net iii

C O M M E N T A R Y 

Holistic Quarterbacking and the 80/20 Rule 


All oncologists face treating patients with 

varying forms of emotional distress. In this 

issue of the Journal of Supportive Oncology, 

Morgans and Schapira’s “Recognizing Depression 

in Patients with Cancer” offers a front-line approach 

to treat clinically depressed cancer patients. 

Meyer and Block’s “Personality Disorders in the 

Oncology Setting” provides oncology clinicians 

with practical tips on how to manage behaviorally 

“difficult” patients. 

Morgans and Schapira define cancer treatment 

as a marathon. Oncologists have the perspective of 

seeing their patient as a “whole person” over time. 

They can pick up on potential indicators of emerging 

depressed mood, such as distress and loss of 

characteristic resiliency. Similarly, as the “quarterbacks” 

of well-functioning interdisciplinary teams, 

oncologists can screen for depression and initiate 

pharmacotherapy before calling in the special 

teams (psychiatry, psychology, social work, clergy, 

etc.). 

Not all oncologists are ready to embrace the 

holistic quarterback approach. Fortunately, the National 

Comprehensive Cancer Network’s guidelines 

for distress management 1 identify screening 

for distress as a clinical responsibility of the system 

of care, not of the individual physician. 

The tools used to clinically assess a patient’s 

distress are a guide for further exploration and rational 

treatment. As Morgans and Schapira note, 

there is an overlap in some cancer patients of disease- 

and treatment-related symptom burden. Depression 

symptoms such as fatigue, sleep changes, 

appetite loss, and cognitive effects confound traditional 

diagnostic methods in the same way personal, 

cultural, linguistic, and other variables complicate 

the picture. Under such circumstances, 

even experienced psycho-oncologists find diagnostic 

clarity elusive. 

“Personality Disorders in the Oncology Setting” 

provides non-mental health oncology clinicians a 

Correspondence to: Thomas Strouse; e-mail: TStrouse@mednet. 

ucla.edu 

J Support Oncol 2011;9:43 © 2011 Published by Elsevier Inc. 

doi:10.1016/j.suponc.2011.02.006 

vocabulary and clinical concepts to understand and 

manage “difficult” patients. These patients often 

prove the “80/20” rule; i.e., “20% of the people 

drive 80% of my work.” They frustrate, antagonize, 

and occasionally frighten the clinicians who care 

for them. The challenge for the authors was to 

write a review that would not do the same to their 

readers. 

Meyer and Block integrate basic concepts about 

the clinician–patient relationship. The clinician 

may have a need for control or a wish to be idealized. 

The patient may have issues with dependence, 

trust, abandonment, abuse, etc. These foundational 

concepts then blend with “modern” variations. For 

example, a patient who takes a consumerist approach 

to his or her cancer crisis and relies on 

Web-based pseudomedical information risks reducing 

the treatment relationship to a technician– 

consumer dyad. This perspective can be destructive 

for the personality-disordered patient who is anxious, 

demanding, and unselective about data 

sources and has difficulty investing trust in or developing 

dependence on a doctor. Add to this a 

physician who defaults under stress toward authoritarianism, 

impatience, avoidance, or hostile defensiveness 

and you have the opposite of a match 

made in heaven. 

Residents, fellows, and recently minted subspecialist 

physicians may find it easier, under circumstances 

of conflict and complex medical decision 

making, to neutrally offer choices and do as they 

are bid. For the personality-disordered patient, this 

approach may inadvertently fuel chaos. I worry that 

a generation of physicians has somehow gotten the 

message it is not appropriate to say “no” or to 

impose behavioral requirements as a condition of 

treatment. Meyer and Block provide us with the 

tools to achieve a significantly more nuanced view: 

It is hard work, but the benefits for patients and 

staff alike are worth it. 

Conflicts of interest: None to disclose. 

References PubMed ID in brackets 

1. Holland JC, Andersen B, Breitbart WS, et al. Distress 

management. J Natl Compr Canc Netw 2010;8(4):448–485. 

Dr. Strouse is Professor 

of Clinical Psychiatry, 

Maddie Katz Endowed 

Chair in Palliative Care 

Research and 

Education, Medical 

Director Stewart and 

Lynda Resnick 

Neuropsychiatric 

Hospital at UCLA, 

Vice-Chair for Clinical 

Affairs, Department of 

Psychiatry and 

Biobehavioral Sciences, 

David Geffen-UCLA 

School of Medicine, Los 

Angeles, California. 

VOLUME 9, NUMBER 2 � MARCH/APRIL 2011 www.SupportiveOncology.net 43

R E V I E W 

Personality Disorders in the Oncology 

Setting 

Fremonta Meyer, MD, and Susan Block, MD 

Approximately 10%–30% of patients treated 

in health care settings exhibit difficult behavior, 

1–3 although the specific frequency 

of difficult patient encounters in oncology practices 

is unknown. Difficult behavior takes many 

forms in the cancer setting: arriving late or not 

showing to appointments, intense questioning of 

the clinician, frequent pages or e-mails between 

appointments, and angry or demanding behavior 

toward staff and clinicians. Difficult patients tend 

to evoke negative emotions, such as guilt, irritation, 

anxiety, and aversion, in the clinician. They 

often have difficulties forming effective treatment 

alliances with both medical and mental health providers 

and may exhibit treatment nonadherence 

and high resource utilization. Although most cancer 

patients with high levels of psychological distress 

benefit significantly from supportive counseling 

with a mental health clinician, either difficult 

patients refuse referrals or their emotional symptoms 

are refractory to usual therapeutic interventions 

and medication management. Some difficult 

patients may even threaten legal action because of 

perceived medical negligence or improper treatment. 

4 Many, but not all, difficult patients have 

personality disorders; cultural differences, other psychiatric 

disorders, circumstances, and relational 

styles can also manifest themselves as difficult behavior. 

Physician (or clinician) factors, as well as 

the challenges and frustrations of negotiating the 

health care system, may also contribute to patients’ 

difficult behaviors in the health care setting. 

This article will briefly address differential diagnosis 

of the difficult oncology patient and then 

describe the definition, subtypes, and clinical as- 

Manuscript submitted September 19, 2010; accepted January 

6, 2011. 

Correspondence to Fremonta Meyer, MD, Department of 

Psychosocial Oncology and Palliative Care, Dana-Farber 

Cancer Institute, 44 Binney Street SW411, Boston, MA 

02115; telephone: (617) 632-4566; fax: (617) 632-6180; 

e-mail: flmeyer@partners.org 

J Support Oncol 2011;9:44–51 © 2011 Elsevier Inc. All rights reserved. 

doi:10.1016/j.suponc.2011.02.002 

Abstract This paper reviews differential diagnosis of the difficult oncology 

patient, focusing on how the patient, the physician, the patient– 

physician relationship, and the cancer setting may all contribute to 

clinicians’ experiences of difficulty. Because many difficult patients have 

personality disorders, we then review assessment and treatment of the 

personality disorders that tend to be most problematic in the cancer 

setting, suggesting a team-based approach for management. 

sessment of personality disorders. We will also examine 

how clinicians may contribute to or ameliorate 

difficult dynamics and describe treatment 

strategies targeting both the difficult patient and 

the treatment team in the oncology setting. 

Differential Diagnosis of the Difficult 

Oncology Patient 

All physician–patient relationships are influenced 

by the dynamics within the patient, within 

the physician, and within the relationship. Cancer 

and its associated treatments further determine 

the nature of the physician–patient relationship, 

often intensifying it and heightening its 

stakes. Optimal care requires open-minded attention 

to all of these areas (Table 1) when a difficult 

interaction or relationship occurs. 

THE PATIENT 

In a study of 500 adults presenting to a primary 

care clinic, Jackson and Kroenke 3 found 

that patients labeled “difficult” were more likely 

to have a depressive or anxiety disorder, poor 

functional status, and greater use of health care 

services. Patients with unmet expectations were 

more likely to be seen as difficult. 3 In the oncology 

setting, patients with anxiety disorders may 

be problematic when their high anxiety prevents 

them from retaining information about treatment 

options; they may then ask questions that 

make it appear they were not listening. Additional 

patient factors associated with being perceived 

as difficult include untreated psychotic 

disorders, organic brain disorders (particularly 

Dr. Meyer is from the 

Department of 

Pscychiatry at the 

Dana-Farber Cancer 

Institute Psychosocial 

Oncology Program, and 

is an instructor at 

Harvard Medical 

School, Boston, MA. 

Dr. Block is Chair and 

Professor of Psychiatry, 

Psychosocial Oncology 

and Palliative Care, 

Dana-Farbar Cancer 

Institute, Brigham and 

Women’s Hospital, 

Harvard Medical 

School. 

44 www.SupportiveOncology.net THE JOURNAL OF SUPPORTIVE ONCOLOGY

Table 1 

Factors Contributing to Difficult Patient Encounters 

in the Oncology Setting 

FACTOR EXAMPLE 

Patient Depression, anxiety, somatization, substance 

abuse, personality disorder, delirium, psychosis 

Physician High need for control, high expectation of 

physician’s role, perceived lack of time to 

address or lack of interest in psychosocial 

concerns, difficulty setting limits 

Patient–physician 

relationship 

Cultural or socioeconomic mismatch, mismatch 

in information-sharing preferences, “patient” vs 

“consumer” 

Cancer context “Life-or-death,” pressure on clinician for a 

“save,” disinhibiting adjuvant medications, 

intense treatment demands and schedules, 

high expectations fostered by tertiary care 

ethos 

undetected mild delirium), trauma, and substance abuse/dependence. 

Other studies have reported a higher prevalence of 

multiple somatic symptoms 1 and personality disorders 5 in difficult 

patients. Patients with chaotic lifestyles resulting from 

poverty or patients from non-Western cultures who have 

different expectations of medical care may be less adherent to 

treatments, miss appointments, or arrive late, contributing to 

difficult encounters. 

THE PHYSICIAN 

Physicians bring many qualities and attitudes to the physician–patient 

relationship that can make patients seem more 

or less difficult. Physicians may dislike engaging with patients’ 

psychosocial challenges and concerns for personal or financial 

reasons (eg, discomfort with intense emotion, nonreimbursement 

for time spent talking about psychosocial issues). Less 

psychosocially minded physicians 6 were more likely to experience 

difficult patient encounters as classified by the difficult 

doctor–patient relationship questionnaire, a validated instrument 

in primary care. 2,3 Physician views about “appropriate” 

patient behavior, their abilities to tolerate strong emotions in 

their patients, perceptions of their own responsibility, and 

level of control of patient outcomes are among many factors 

that can contribute to physicians’ views of patients as difficult. 

For example, when a patient has refractory pain/nausea/dyspnea 

or other symptoms that are unresponsive to usual treatments, 

some physicians may feel incompetent and respond to 

those feelings by distancing themselves and making the patient 

more anxious, thus contributing to behavioral disturbance. 

Difficult patients tend to provoke strong emotions in 

their clinicians, a phenomenon known as countertransference. 

When unrecognized, these emotions can prompt a variety 

of maladaptive clinician responses: distancing or becoming 

overinvolved with the patient, lack of empathy for the 

patient, failing to set appropriate limits on patient behavior, 

or, in some cases, even inappropriately terminating the patient’s 

care. Difficulty tolerating a patient’s expressed emo- 

Meyer and Block 

tions such as tearfulness or anxiety may lead the physician to 

ignore emotions, thereby eliciting amplification of the patient’s 

emotional expression with the goal of engaging the physician. 

Lack of awareness of one’s own negative emotions such as anger 

may contribute to a physician being curt, brusque, or distant. 

Finally, clinicians who are particularly attached to the ideal of 

physicians as inexhaustible caregivers, giving their utmost for 

each patient, may be uncomfortable setting limits on patients’ 

unreasonable demands. Especially in the setting of advanced and 

terminal cancer, clinicians can feel powerless to alter the course 

of the illness and may view gratifying these demands as the only 

way to be of help to a dying patient. 

THE PHYSICIAN–PATIENT RELATIONSHIP 

Patients and physicians may also have different models of 

the physician–patient relationship that create tension and 

conflict. Until the mid-20th century, physician–patient relationships 

were asymmetric, reflecting a paternalistic model, 

with the physician using his/her power and influence for the 

good of the patient. This idealistic model further assumes 

patient trust, physician availability, and physician connections 

with the patient’s family and community. 7 However, 

other theorists suggest that, as the power differential increases 

because of advances in medical technology and exploding 

medical information, abuses of power may increase. 8 In recent 

years, the consumer metaphor, in which the doctor–patient 

relationship is viewed as an economic transaction and health 

care as a commodity, has coexisted with the paternalistic 

metaphor. The patient may demand “fair value,” with attention 

to his/her rights and preferences, and the presentation of 

all relevant treatment options. 9 Physicians and patients therefore 

may have divergent ideas about the boundaries of a 

treatment relationship. For example, certain patients, encouraged 

by family/friends and the popular press to advocate for 

themselves, may question the physician both in person and by 

e-mail about additional treatment options they have researched 

on the Internet, causing some physicians to feel 

demeaned and to perceive that their expertise is in question. 

Conversely, those patients who have a more traditional, paternalistic 

view of the doctor–patient relationship or who are 

seriously regressed because of their illness may experience 

difficulty with a physician who presents the risks and benefits 

of several complex treatment protocols and then expects the 

patient to evaluate this detailed information and decide. Recent 

work on the doctor–patient relationship has suggested, 

perhaps not surprisingly, that doctors better understand their 

patients’ health beliefs when patients actively participate in 

the consultation by asking questions and sharing concerns 

and also that physicians are poorer judges of their patients’ 

attitudes when patients are of a different race or culture than 

the physician. 10 

THE CANCER SETTING 

Patients confronting a cancer diagnosis—no matter what 

stage—immediately perceive its life-threatening nature. High 

levels of fear and anxiety often result in correspondingly high, 



even unrealistic, expectations of physicians, producing pressure 

to “save” patients. This phenomenon is particularly common 

in tertiary care cancer centers whose ethos tends toward 

an emphasis on “hope” and patient-centered care without 

limits. In the setting of high-risk primary tumors, clinicians 

may feel compelled to tolerate harassing, devaluing, and even 

treatment-interfering (self-injurious, alcohol, or substance 

abuse) behaviors in the interest of “getting the patient 

through their chemotherapy or radiation.” Complex oncologic 

treatment schedules may extrude prior treaters such as 

primary care doctors and community mental health providers, 

contributing to insufficient understanding of patients and 

even splitting among caregivers unless collateral information 

is reliably obtained. Likewise, the multidisciplinary nature of 

cancer care, involving regular contact with many providers, 

can contribute to mixed messages and splitting. Adjuvant medications 

used in the oncology clinic such as steroids, benzodiazepines, 

and opioids may contribute to behavioral disinhibition. 

Finally, we have observed that early-phase research protocols at 

tertiary cancer centers (albeit offering exciting opportunities for 

response and even prolonged survival in metastatic disease) may 

further foster unrealistic expectations in a subset of psychologically 

vulnerable patients. 

A thoughtful differential diagnosis, examining the multiplicity 

of factors that could contribute to difficulty, is key to 

developing an appropriate treatment strategy for the patient. 

In subsequent sections, we will focus on the challenges represented 

by patients who have personality disorders and require 

specialized approaches to their management. 

Personality Disorders 

Personality disorders involve a severe, stable pattern of 

inner experiences and behavior that deviates markedly 

from the expectations of the individual’s culture. The disorder 

has a pervasive effect on cognition, affective responses, 

interpersonal relationships, and/or impulse control 

and leads to subjective distress or impairment in social and 

occupational functioning. 11 The patterns of behavior cannot 

be better explained by another psychiatric or a general medical 

disorder, including the effects of alcohol or drugs (including 

prescribed medications). 11,12 In general, such patients are 

not troubled by their own behaviors, which are ego-syntonic, 

and blame others for their problems. 

In the general population, prevalence rates of personality 

disorders range from 10% to 14%. 13 There is a high degree of 

comorbidity between distinct personality disorders. 14,15 Research 

on gender differences has been inconsistent. The National 

Epidemiological Surveys of Alcohol and Related Conditions 

study, including more than 30,000 subjects, 14–17 

reported prevalence rates ranging from 0.5% for dependent 

personality to 7.9% for obsessive-compulsive personality. Of 

note, borderline personality had a prevalence of 5.9% with no 

differences in rates among men and women. 14 Without contrary 

research evidence to date, we assume that the prevalence 

rate in cancer patients is about the same as that in the 

general population. 18 

Common Features of Personality Disorders 

Patients with personality disorders often have childhood 

histories involving emotionally distant or neglectful caregivers, 

even sexual and physical abuse, and are subsequently at 

high risk for domestic violence in their intimate relationships. 

Adverse life events and genetic factors appear to interact as 

risk factors for development of the personality disorders. A 

serious medical illness such as cancer often leads to intensification 

of feelings of vulnerability, which exacerbates underlying 

fears of neglect and abandonment and may lead to 

behavioral regression and further illumination of personality 

vulnerabilities. 12 

In stressful medical settings, these patients are prone to utilize 

primitive defenses such as splitting, denial, and projective identification. 

In splitting, the patient rigidly separates medical caregivers 

into “good ones” and “bad ones” and often cooperates with 

those perceived as good while rejecting the advice of those 

perceived as bad. As a result, different medical staff members 

experience the patient differently, which may result in inconsistent 

patient care and discord among the treatment team. There 

may be disagreements on the patient’s level of pain, veracity of 

reported physical symptoms, and the need for psychotropic medication. 

19 Patients often shift their alliances in ways that are 

obscure to the clinician, creating instability in their behaviors. 

Some patients also engage in pathological denial, leading them 

to refute the details or even knowledge of their illness, 20 decide 

suddenly that they are cured, or flee from medical treatment. 

The opportunities often afforded by the cancer treatment setting 

to switch providers, get second opinions at other institutions, 

and ultimately even divide care across several institutions can 

further fuel denial or splitting behaviors. 

In projective identification, the patient projects an emotion, 

such as anger, onto the clinician, who then “acts out” the same 

emotion by retaliating against the patient. For example, a patient 

who in the past has alienated friends and co-workers because of 

angry outbursts may speak in a loud voice, curse, intimidate 

clinic support staff, and even verbally devalue the clinician who 

is attempting to collect medical information. The clinician experiences 

anger that is transferred from the patient and is at high 

risk of failing to acknowledge the patient’s emotional distress and 

current symptoms. This experience may lead to various behaviors 

by the clinician, such as redirecting anger toward colleagues, 

angry outbursts to the patient, overly punitive limit setting, or 

rejection of the patient. 

In addition to posing challenges in managing interpersonal 

behaviors, patients with personality disorders are at 

risk for suicidal ideation and attempts. In particular, 75% 

of patients with borderline personality disorder attempt 

suicide at least once. Approximately 10% eventually complete 

suicide, with prior attempts, hopelessness, impulsivity, 

and substance abuse increasing the risk of completed 

suicide. 21 For this reason, clinicians must assess for the 

presence of suicidal ideation, intent, or plan and act decisively 

to assure the patient’s safety. 


Specific Personality Disorders 

Some types of personality disorders tend to be reasonably 

well-tolerated in the medical setting, as noted below. 

SCHIZOID AND SCHIZOTYPAL PERSONALITY DISORDERS 

Schizoid patients tend to be detached from others and indifferent 

to intimate relationships; schizotypal patients are awkward 

in their interpersonal relationships and demonstrate perceptual 

distortions and eccentric ideas and behavior. Clinicians often 

have trouble connecting with these patients and may note that 

they seem “strange” and are socially isolated, but in general these 

disorders are less difficult in the medical setting because of the 

patients’ isolation and distance from clinicians. 

AVOIDANT PERSONALITY DISORDER 

Avoidant patients display social inhibition, feelings of inadequacy, 

and hypersensitivity to negative evaluation. Although 

their traits limit socialization and intimate relationships, 

avoidant patients are tolerated relatively easily in the 

medical setting because their internal difficulties are usually 

carefully protected and not shared with the clinician; however, 

they often lack the social support that helps patients 

through the cancer experience. 

Table 2 describes characteristics of the individual personality 

disorders that are problematic in medical settings (paranoid, 

antisocial, narcissistic, borderline, histrionic, dependent, obsessive-compulsive) 

with observed behaviors, clinician countertransference, 

possible therapeutic responses, and medication 

treatment strategies. 

MIXED PERSONALITY DISORDERS 

Many difficult patients exhibit characteristics of more than 

one personality disorder. For example, at our institution, a young 

female survivor of a high-risk breast cancer allowed transfer of 

some, but not all, prior treatment records from other cancer 

centers. She became angry when her new psychiatrist placed 

restrictions on the prescription of controlled substances, including 

a requirement for prior collateral history, and fired that 

psychiatrist (paranoid). Toward her prior psychiatrist, who had 

previously prescribed the medication, she expressed in one moment 

idealization of his empathy and professionalism and in the 

next moment a desire to file a complaint or even sue him for not 

providing the standard of care (borderline). She further stated 

that as a “cancer patient” she deserved considerations, including 

prompt attention even if she arrived hours late for an appointment 

(narcissistic). 

Assessment of Personality Disorders 

Clues to a personality disorder diagnosis include multiple 

lifetime changes in jobs, relationships, and personal goals and 

personal histories including substance abuse, physical and sexual 

abuse, or childhood neglect. The clinician’s feelings of confusion, 

anger, frustration, boredom, or anxiety may also signal 

personality pathology, and it is important for clinicians to pay 

attention to their own emotional responses as “data.” For example, 

boredom often provides a clue to narcissistic personality, in 


which the patient uses the clinician as a “sounding board” (eg, 

behaving as if the clinician has nothing more important to do 

than listen to the patient talk at length about his/her concerns), 

or obsessive-compulsive traits, in which the patient provides 

excessively detailed information. Under stress, borderline patients 

may appear disorganized, psychotic, or out of touch with 

reality, although without obvious hallucinations or delusions, 

prompting confusion in the clinician. Anger in the clinician 

tends to signal narcissistic or borderline pathology in the patient. 

Poor responses to usual treatment for anxiety and depression, 

frequent reports of suicidal ideation, or frequent unusual side 

effects and patient misunderstandings are other potential indicators 

of character disorders. 

Treatment of Patients with Personality Disorders 

GENERAL PRINCIPLES 

Basic elements of management for all personality disorders 

include the following: 

● building and maintaining a therapeutic alliance; 

● acknowledging and empathizing with the real stresses in 

the patient’s situation; 

● setting realistic expectations for outcomes; recognizing that 

therapeutic goals are best limited to containment of behavior 

and not improvement and that protection of team relationships 

from the negative effects of the patient needs to be a central goal; 

● containment of dangers to patient and staff through setting of 

consistent, appropriate expectations, limits, and boundaries; 

● evaluating for suicidality and homicidality and acting to 

protect the patient and/or potential victims; and 

● ongoing reflection on how the clinician’s clinical response 

to the patient is affected by his/her own emotions. 

When a patient is suspected of having a personality disorder, 

a mental health clinician should be consulted even if the 

patient refuses to be seen by the consultant; the consultant 

can help clarify the dynamics and diagnosis of the patient, 

help medical clinicians tailor their approach to the patient’s 

particular personality style, 12 set appropriate limits and 

boundaries with the patient, guide team meetings to reduce 

conflict among staff, and provide education and emotional 

support to the team. 19 

When an on-site clinician is unavailable, teams should 

obtain the patient’s written consent to collaborate with any 

existing mental health treaters. Convening the oncology 

team to discuss the care of the patient can help clinicians stay 

on the same page, reduce staff conflict, support a unified 

treatment plan, contain dysfunctional patient behavior, and 

support the treatment team in a way that allows them to help 

the patient through treatment. 

MEDICATION 

The focus of medication for patients with personality disorders 

should be on symptom management. Psychotropic 

medications do not alter underlying personality structure, but 

can be effective in treating target symptoms. Selective serotonin 

reuptake inhibitors (SSRIs) are effective for anxiety, 

depression, and irritability, which can also be targeted by 



Table 2 

Personality Disorders and Management Strategies 

PERSONALITY DISORDER CHARACTERISTICS COMMON BEHAVIORS 

Paranoid Suspicious 

Misinterprets others’ actions as 

malevolent 

Antisocial Charming and ingratiating 

Manipulative 

Has history of prior incarceration and 

probation 

Histrionic Flirtatious or sexually provocative 

Intensely emotional (which hinders 

rational discussion of treatment options) 

Dependent Intensely needy, anxious, seeks constant 

reassurance 

Narcissistic Entitled 

Exquisitely sensitive to losses in physical 

vitality and cognitive function 

experienced as a result of cancer therapy 

Behaves well in medical settings when 

needs are being met 

Borderline Emotionally labile 

Fears abandonment 

Frequently changes residences, jobs and/ 

or relationships 

Obsessive-compulsive High need for control, orderliness, and 

perfectionism 

Focuses on details as opposed to the 

larger medical picture 

Depressive (not yet a DSM 

diagnosis, but under 

consideration for the next 

edition of DSM) 

Resists medications or chemotherapy 

Mistrusts clinician 

Threatens legal action 

Tells compelling stories about life difficulties 

that induce the clinician to offer extra help or 

favors; stories later turn out to be lies 

Brings weapons to the clinic 

Provides vivid or exaggerated descriptions of 

physical symptoms 

Has difficulty tolerating delay or ambiguity 

Amplifies somatic complaints, prompting 

workups 

Calls and pages frequently between 

appointments 

Has difficulty making independent decisions 

Has trouble leaving office 

Fails to ask appropriate questions 

Feels a need for intensive care and support, 

including hospitalization, to get through cancer 

treatment 

Requests special consideration 

Directs even minor concerns to the oncologist 

rather than ancillary staff 

Talks at length to extend appointment times 

Becomes extremely distressed by hair loss, 

sexual function difficulties, “chemobrain,” and 

loss of function 

Displays impulsive behaviors such as self-injury, 

substance abuse, binge eating, or sexual 

promiscuity 

Nonadherent with medications 

Misses appointments 

Develops new symptoms during transition 

points in treatment (active treatment to 

survivorship; active treatment to palliative care) 

because of feelings of abandonment 

Repeatedly questions clinicians regarding small 

details of care 

Keeps meticulous logs of symptoms and 

medications 

Has difficulty tolerating the imperfections of the 

process of receiving medical care 

Interprets events negatively Anticipates the worst: predicts negative 

outcomes of treatment even in the face of 

evidence to contrary 

POSSIBLE 

COUNTERTRANSFERENCE 

RESPONSES PROVOKED IN 

CLINICIAN 

Impatience, anger, fear 

Sympathy followed by 

anger and fear 

Arousal 

Fatigue 

Fatigue, guilt, aversion 

Anger; wish to 

counterattack 

Depression, self-doubt, 

anger 

Irritation, anger 

Depression, 

therapeutic nihilism 


Table 2 

Personality Disorders and Management Strategies (Continued) 

SUGGESTED THERAPEUTIC RESPONSE ILLUSTRATIVE MANAGEMENT QUOTE 

Take empathic stance, rather than overtly 

challenging patient’s paranoid thoughts 

Acknowledge effects of patient’s actions 

on you and others; set limits without 

unilaterally terminating or dismissing 

patient; protect safety of staff 

Schedule regular follow-up visits 

regardless of symptom levels, 

acknowledge that treatment may not be 

entirely curative 

Set appropriate expectations of doctor– 

patient relationship, acknowledge limits of 

knowledge/skill as well as time and 

stamina, educate patient about cancer 

care 

Do not challenge entitlement, but channel 

it into partnership in providing the best 

care; emphasize and align with patient 

strengths 

Work empathically and diligently but 

lower expectations and adjust goals; set 

nonpunitive limits; share care with other 

clinicians to reduce burnout; regular team 

meetings (including support staff, if 

appropriate) to coordinate plan of care 

Give patient control when possible; 

acknowledge and empathize with 

vulnerability produced by medical illness 

Encourage patient to consider realistic 

possibilities in addition to worst case 

scenarios; enlist family support to help 

patient notice and revise negative 

thoughts; review decisions about stopping 

treatment with colleague to assure that 

you are not giving up prematurely 

Patient: “You are trying to poison me with this 

chemotherapy.” 

Clinician: “I can certainly see why you feel that way. 

Chemotherapy is tough. If I were having chemotherapy I’d 

be suspicious too. Let’s see how we can address the side 

effects.” 

“You are scaring me and my staff. We want to continue to 

treat you, but we need to think about our safety and will 

call security to have you escorted out of the clinic if you 

again talk about having a knife.” 

“The pain may get better than it is now, but you will 

probably always have some flare-ups, because our 

medications are only 80% effective. I don’t think we need 

to get another bone scan right now but I want to monitor 

you closely and would like to see you every 3 months to 

check on this.” 

“I am committed to be with you for your whole cancer 

treatment. When you call me about questions that could 

easily wait until your next appointment, I get stressed, and 

it makes it harder for me to be there in the way I want to 

be for you.” 

“You deserve the best medical care we can give, and that’s 

why I need you to be here on time so that you get the 

most out of our visit.” 

“You have been very successful in managing a large 

company in difficult times; you are the leader of your 

treatment team, and we will do our best to meet your 

goals.” 

“Like you, I’m only human, so when you swear at me, I get 

upset—and that interferes with my thinking clearly about 

what you need.” 

“You are the one who ultimately decides whether you will 

go to an addiction treatment program. I will keep 

encouraging you, but I cannot offer treatments that will be 

unsafe when you are using IV drugs.” 

“I need a psychiatrist on the team to help me take care of 

you, because I get rattled and scared by some of your 

behaviors.” 

“It is really hard to feel so out of control. While you are in 

infusion, I’d like you to keep a careful log of everything 

that you experience with chemo so you can report it to 

me.” 

“What do you think the worst case scenario is? How likely 

is that?” 

“Even though things didn’t work well with this chemo, I 

am not going to give up on you and we have other 

treatments we can try.” 

COMMENTS ON MEDICATION 

MANAGEMENT 

Atypical antipsychotics for paranoia 

Possible risk of diversion of opioids 

and benzodiazepines; avoid the 

latter in favor of atypical 

antipsychotics or SSRIs 

SSRIs for comorbid depression or 

anxiety 

SSRIs or low-dose benzodiazepines 

for comorbid anxiety 

SSRIs for comorbid depression 

Mood stabilizers or antipsychotics 

for mood lability/irritability 

SSRIs for irritability, comorbid 

depression/anxiety; atypical 

antipsychotics for anger/impulsivity 

Avoid benzodiazepines —high risk 

of abuse 

Close monitoring of opioids 

SSRIs for depression or low-dose 

benzodiazepines for comorbid 

anxiety 

Unlike other personality disordered 

patients, these patients are unlikely 

to abuse benzodiazepines or other 

medications 

SSRIs to target depression 




mood stabilizers and anticonvulsants. Anger and impulsivity 

can be treated with antipsychotics, mood stabilizers, and anticonvulsants, 

all of which demonstrate significant positive 

effects. 22 Distorted thinking, particularly suspiciousness, also 

responds well to antipsychotics. Benzodiazepines, because 

they are often disinhibiting, may amplify dysfunctional behavior. 

Because they are used ubiquitously in the cancer 

setting, clinicians should be aware of this potential risk. In 

general, for patients with suspected personality disorders antipsychotics 

are a safer and more effective treatment for anxiety 

than benzodiazepines. 23 In the absence of prior hypomanic 

or manic symptoms, SSRIs are a good first-line choice 

for depression, anxiety, and irritability; when potential hypomanic 

or manic symptoms, or explosive anger, are present, it 

is best to start with an atypical antipsychotic or mood stabilizer. 

Medication of patients with personality disorders generally 

requires the expertise of a psychiatrist. 

PSYCHOTHERAPY 

Although clearly a valuable treatment for patients with 

personality disorders, psychotherapy aimed at altering underlying 

maladaptive personality patterns can often take years to 

be effective and thus is often not feasible for patients with 

advanced disease. Therefore, it is important for clinicians in 

the cancer setting to lower their expectations and focus on 

behavioral containment. Patients who agree to mental health 

treatment can benefit from having a structured supportive 

psychotherapy, where a clinician allows ventilation of negative 

emotions, assists in reality testing, follows up on behavioral 

plans and expectations, and helps the patient work 

through challenges in receiving medical treatment. For patients 

with a favorable medical prognosis, dialectical behavioral 

therapy has been shown to reduce self-injurious behaviors 

and help patients to modify their interpersonal 

communication styles, regulate their emotions, and tolerate distress 

without acting out destructively. 24 

Because difficult patients often contribute to clinician distress 

and burnout, it is also important to take time for personal 

reflection, debrief with other team members, and/or share the 

patient’s care with a colleague. A trusted colleague can help the 

treating clinician gain perspective, avoid being pulled into dysfunctional 

responses, and serve as a sounding board for the 


1. Hahn SR. Physical symptoms and physicianexperienced 

difficulty in the physician–patient relationship. 

Ann Intern Med 2001;134:897–904. 

[11346326] 

2. Hahn SR, Thompson KS, Wills TA, Stern V, 

Budner NS. The difficult doctor–patient relationship: 

somatization, personality and psychopathology. 

J Clin Epidemiol 1994;47:647– 

657. [7722577] 

3. Jackson JL, Kroenke K. Difficult patient encounters 

in the ambulatory clinic: clinical predic- 

frustration that often is part of treating patients with personality 

disorders. 

Conclusion: Difficult Patients, Difficult Doctors, 

and Difficult Dyads 

Clinicians must consider their own personal vulnerabilities 

and countertransference responses as well as constraints imposed 

by time limitations and productivity requirements before 

simply labeling a patient as difficult. In addition, feelings 

of helplessness in halting the progression of cancer, disappointment 

at new treatments that prove to be ineffective, 

discomfort with patient decisions to undertake end-of-life 

treatments that are futile, and impatience with the challenges 

of patients coming to terms with death and dying may all 

contribute to the clinician’s perception that a patient is difficult. 

The patient who demands a specific tumor marker 

blood test or a regular breast magnetic resonance imaging in 

the face of research evidence to the contrary may be “narcissistic,” 

may have an anxiety disorder, and/or may be operating 

within a “consumer advocate” model of the doctor–patient 

relationship that sometimes encourages unrealistic expectations 

of health care delivery. Based on personal life experiences, 

some clinicians find entitled, angry, narcissistic patients 

particularly distressing, whereas others are more 

troubled by intense dependency and neediness. Developing 

insight into one’s personal triggers promotes less impulsive 

and more thoughtful responses to difficult patients. Setting 

appropriate boundaries and not being pulled into offering 

“special treatment” for certain patients protects clinician morale 

and the integrity and sustainability of the physician– 

patient relationship. Curiosity can also be an effective stance 

in dealing with a patient who has a personality disorder. In 

seeking to understand and explore the patient’s behavior, the 

clinician conveys a sense of interest and connection while 

providing some intellectual distance from the emotional intensity 

of the patient. Finally, clinicians should make every 

effort not to suffer alone with challenging patients. Consultation 

and team-based care both protect physicians and improve 

patient outcomes. 

Acknowledgments: We acknowledge the thoughtful comments 

of Dr. John Peteet on the manuscript. 


tors and outcomes. Arch Intern Med 1999;159: 

1069–1075. [10335683] 

4. Wasan AD, Wootton J, Jamison RN. Dealing 

with difficult patients in your pain practice. 

Reg Anesth Pain Med 2005;30:184–192. 

[15765460] 

5. Schafer S, Nowlis DP. Personality disorders 

among difficult patients. Arch Fam Med 1998;7: 

126–9. [9519915] 

6. Ashworth CD, Williamson P, Montano D. A 

scale to measure physician beliefs about psy- 

chosocial aspects of patient care. Soc Sci Med 

1984;19:1235–1238. [6523166] 

7. Parsons T, Fox R. Illness, therapy and the 

modern urban American family. J Social Issues 

1952;8:31–44. 

8. Freidson E. Professional Dominance: The 

Social Structure of Health Care. New York: Atherton 

Press; 1970. 

9. Potter SJ, McKinlay JB. From a relationship 

to encounter: an examination of longitudinal 

and lateral dimensions in the doctor- 


patient relationship. Soc Sci Med 2005;61:465– 

479. [15893060] 

10. Street RL, Haidet P. How well do doctors 

know their patients? Factors affecting physician 

understanding of patients’ health beliefs. J Gen 

Intern Med 2011;26:21–7. [20652759] 

11. American Psychiatric Association. Diagnostic 

and Statistical Manual of Mental Disorders. 

Washington, DC: American Psychiatric Press; 2000. 

12. Kahana RJ, Bibring GL. Personality types 

in medical management. In Zinberg NE, eds. 

Psychiatric and Medical Practice in a General 

Hospital. New York: International Universities 

Press; 1964. 

13. Torgerson S. Epidemiology. In: Oldham 

JM, Skodal AE, Bender DS, eds. Textbook of Personality 

Disorders. Washington, DC: American 

Psychiatric Publishing, Inc; 2005. 

14. Grant BF, Chou SP, Goldstein RB, Huang 

B, Stinson FS, Saha TD, Smith SM, Dawson DA, 

Pulay AJ, Pickering RP, Ruan WJ. Prevalence, correlates, 

disability, and comorbidity of DSM-IV 

borderline personality disorder: results from the 

Wave 2 National Epidemiologic Survey on Alcohol 

and Related Conditions. J Clin Psychiatry 

2008;69:533–545. [18426259] 

15. Grant BF, Hasin DS, Stinson FS, Dawson 

DA, Chou SP, Ruan WJ, Pickering RP. Prevalence, 

correlates, and disability of personality disorders 

in the United States: results from the national 

epidemiologic survey on alcohol and related 

conditions. J Clin Psychiatry 2004;65:948–958. 

[15291684] 

16. Pulay AJ, Stinson FS, Dawson DA, Goldstein 

RB, Chou SP, Huang B, Saha TD, Smith SM, Pickering 

RP, Ruan WJ, Hasin DS, Grant BF. Prevalence, 

correlates, disability, and comorbidity of DSM-IV 

schizotypal personality disorder: results from the 

Wave 2 National Epidemiologic Survey on Alcohol 

and Related Conditions. Prim Care Companion 

J Clin Psychiatry 2009;11:53–67. [19617934] 

17. Stinson FS, Dawson DA, Goldstein RB, 

Chou SP, Huang B, Smith SM, Ruan WJ, Pulay AJ, 

Saha TD, Pickering RP, Grant BF. Prevalence, correlates, 

disability, and comorbidity of DSM-IV 

narcissistic personality disorder: results from the 

wave 2 national epidemiologic survey on alcohol 

and related conditions. J Clin Psychiatry 

2008;69:1033–45. [18557663] 

18. Derogatis LR, Morrow GR, Fetting J, Penman 

D, Piasetsky S, Schmale AM, Henrichs M, 

Carnicke CL Jr. The prevalence of psychiatric 

disorders among cancer patients. JAMA 1983; 

249:751–757. [6823028] 

19. Hay JL, Passik SD. The cancer patient 

with borderline personality disorder: suggestions 

for symptom-focused management in 


the medical setting. Psychooncology 2000;9: 

91–100. [10767747] 

20. Weitzner MA, Lehninger F, Sullivan D, 

Fields KK. Borderline personality disorder and 

bone marrow transplantation: ethical considerations 

and review. Psychooncology 1999;8:46– 

54. [10202782] 

21. Black DW, Blum N, Pfohl B, Hale N. Suicidal 

behavior in borderline personality disorder: 

prevalence, risk factors, prediction, and 

prevention. J Pers Disord 2004;18:226–239. 

[15237043] 

22. Mercer D, Douglass AB, Links PS. Metaanalyses 

of mood stabilizers, antidepressants 

and antipsychotics in the treatment of borderline 

personality disorder: effectiveness for depression 

and anger symptoms. J Pers Disord 

2009;23:156–74. [19379093] 

23. Saunders EF, Silk KR. Personality trait dimensions 

and the pharmacological treatment of 

borderline personality disorder. J Clin Psychopharmacol 

2009;29:461–467. [19745646] 

24. Linehan MM, Comtois KA, Murray AM, et 

al. Two-year randomized controlled trial and follow-up 

of dialectical behavior therapy vs therapy 

by experts for suicidal behaviors and borderline 

personality disorder. Arch Gen Psychiatry 

2006;63:757–66. [16818865] 


P E E R V I E W P O I N T 

Personality Disorders in the Clinical 

Setting 

Walter F. Baile, MD 

Commentary on “Personality Disorders in the 

Oncology Setting” by Meyer et al (page 44) 

Although the title of this paper suggests 

that its main focus is on personality 

disorders, it paints a much broader 

picture because it discusses aspects of patient 

behavior that clinicians often find challenging 

to manage. “Difficult patients” often evoke 

negative emotions in the caregiver or disrupt 

their own continuity of care by refusing 

treatment, angry outbursts, or demanding 

requests of the treatment staff. An important 

step not easily achieved is to recognize when it 

is the stress of illness that is exaggerating 

normal traits or when the patient has a more 

pervasive and lifelong disorder. 

In many cases the stresses of the cancer illness 

are responsible for amplification of traits, such as 

passivity in the person with a dependent personality 

or exaggerated attention to details exhibited 

by the obsessive compulsive personality. Recognition 

of these traits can allow the clinician to 

adjust his or her behavior to the patient’s needs. 

For example, persons with narcissistic traits (Table 

2) may be particularly prone to loss of selfesteem 

and depression when they undergo disfig- 

Correspondence to: Walter F. Baile, MD, Program in Interpersonal 

Communication and Relationship Enhancement, 

University of Texas MD Anderson Cancer Center, Houston, 

TX 77030; telephone: (713) 745-4116; fax: (713) 794-4236; 

e-mail: wbaile@mdanderson.org 


doi:10.1016/j.suponc.2011.02.003 

uring surgery. Acknowledging the challenge the 

illness presents to patients and praising them for 

their perseverance may be a useful strategy. The 

authors also point out that it is important for 

busy clinicians not to be annoyed with patients 

who require more time or patience. 

A more serious problem is represented by the 

5%–8% of the population affected by a personality 

disorder such as antisocial behavior or borderline 

personality. These patients are often 

challenging in the oncology setting because their 

behaviors may be more disruptive than that of 

patients with exaggerated personality traits. Acting 

out in the form of aggressive behavior or 

unexpected anger at staff can be particularly 

troublesome. In the case of the borderline disorder, 

patients may pit staff against one another or 

engage in other behaviors, as outlined by the 

authors (Table 2). In my experience, the clinic 

and especially the inpatient staff have great difficulty 

in distinguishing these two situations. Patients 

are allowed to seriously act out before help 

in managing the individual is requested. It is 

important to pay attention to clues that might 

suggest a more serious disorder. For example, 

substance abuse revealed through a patient’s personal 

history would be a clue for a borderline or 

antisocial personality. When serious disruptive 

behavior does occur, early consultation by mental 

health professionals can help define the diagnosis 

and provide management strategies for the 

treatment team and support for the staff, who 

often feel frustrated with their ability to manage 

such problems. 

Dr. Baile is affiliated 

with the Program in 

Interpersonal 

Communication and 

Relationship 

Enhancement at the 

University of Texas MD 

Anderson Cancer 

Center, Houston, 

Texas. 


P E E R V I E W P O I N T 

Challenging Personalities in the 

Oncology Setting 

Matthew Doolittle, MD, and Jimmie C. Holland, MD 

Commentary on “Personality Disorders in the 

Oncology Setting” by Meyer et al (page 44) 

The effective treatment of cancer in patients 

who may already suffer from significant heart 

disease, poorly controlled diabetes, or another 

chronic medical condition requires extra 

monitoring, consultation, or even modification of 

chemotherapy regimens or surgeries. In general, 

however, these additional needs are known to the 

oncologist through clear history, symptoms, and 

objective measures, and oncologists therefore have 

readily available means to manage the attendant 

risks. Patients who have challenging personality 

traits or personality disorders will require as much or 

more attention and management, both for the 

success of their own treatments and for the sake of 

minimizing disruption in the clinic itself. 

The needs of these patients are usually less clear to 

the oncologist at the outset. Many personality disorders 

remain unidentified in the community or are 

never treated consistently, and the oncologist may 

have no knowledge of the diagnosis. Further complications 

may arise when a patient’s challenging personality 

traits that have not previously risen to a diagnosable 

disorder worsen under the stress of a cancer 

diagnosis. The patient’s behavior may become increasingly 

disordered at precisely the time when the 

challenges of a serious illness require the most fully 

adaptive response. In this issue, Meyer and Block 1 

address the problem of personality disorders in the 

oncology setting and offer some practical information 

about identifying and responding to the needs of these 

patients. 

The complexity of diagnosing personality disorders 

is suggested by the multiple classification systems 

summarized by the authors (ie, formal diagnostic 

criteria for individual disorders, “clusters” of 

personality traits, as well as descriptive categories 

Correspondence to: Matthew Doolittle, Memorial Sloan- 

Kettering Cancer Center, 1275 York Avenue, New York, NY 

10065. E-mail: doolittm@mskcc.org 


doi:10.1016/j.suponc.2011.02.004 

that emerge from James Groves’ classic work on 

“the hateful patient” 2 ). Whatever the challenges of 

describing personality disorders, they differ from 

other chronic conditions complicating cancer care 

in one important way: the emotional responses of 

physicians themselves provide information that can 

be critical to identifying and managing the problem. 

Meyer and Block provide a useful summary of observable 

behaviors and emotional reactions in both patients 

and clinicians, along with general principles and 

particular responses for managing those behaviors. 

Of equal interest to the oncologist is the question 

of when managing such behaviors will require psychiatric 

involvement for treatment of the patient, for 

advice to staff, or both. Although the authors do not 

address this issue directly, they do introduce the principle 

of regarding the physician’s behaviors and reactions 

as sources of information that are as important as 

observations of the patients themselves. Returning to 

this principle may be the most useful strategy for 

oncologists who might seek psychiatric consultation. 

A psychiatric referral might be helpful when: (1) interactions 

of staff members regarding a particular patient 

become less effective or less professional than 

usual; (2) emotional responses of staff members become 

difficult for themselves or others around them to 

understand or tolerate; or (3) the patient is consuming 

more resources or staff time than the clinic can sustain. 

In general, referral may become not only helpful 

but also necessary if the behaviors of a given patient 

and the responses of staff have reached the point at 

which the patient is no longer receiving the same 

standard of oncology care as other complicated patients. 

Although the literature on personality disorders 

is vast, this summary by Meyer and Block, and in 

particular its emphasis on attending to staff responses, 

may be of use in meeting everyday needs of oncologists, 

oncology clinics, and some of their most vulnerable 

patients. 

References 

1. Meyer F, Block S. Personality disorders in the oncology 

setting. J Support Oncol 2011;44–51. 

2. Groves JE. Taking care of the hateful patient. N Engl 

J Med 1978;298:883–887. 

Dr. Doolittle is with the 

Memorial Sloan- 

Kettering Cancer 

Center, New York 

Presbyterian Hospital 

(Cornell Campus), New 

York, New York. 

Dr. Holland is with the 

Wayne E. Chapman 

Chair in Psychiatric 

Oncology Attending 

Psychiatrist Department 

of Psychiatry and 

Behavioral Sciences, 

Memorial Sloan- 

Kettering Cancer 

Center, New York, New 

York. 


H O W W E D O I T 

Recognizing Depression in Patients with 

Cancer 

Alicia Morgans, MD, and Lidia Schapira, MD 

Caring for the “whole person” across the continuum 

of disease remains a top priority for 

clinicians. Strides in basic and translational 

research have opened new therapeutic pathways 

that provide better targeted and effective treatments 

with fewer side effects. New ancillary and 

supportive therapies have transformed and improved 

the experiences of patients undergoing anticancer 

treatments. Also, with increasing acceptance 

of multidisciplinary teams, we have the 

opportunity to make timely referrals to colleagues 

who provide supportive and palliative care and targeted 

interventions to treat pain and disabling 

symptoms. If our goal is to recognize the full impact 

of cancer on the lives of patients and their families, 

it is important to address the emotional and psychological 

toll of diagnosis and treatment. Recognizing 

depressive symptoms and clinical depression 

is an important step toward optimizing the quality 

of life for patients with cancer. 

Data summarized in an excellent review by 

Pirl published in 2004 show that up to one in five 

Americans will experience depressive symptoms 

over the course of their lifetime and that approximately 

10%–25% of cancer patients meet criteria 

for clinical depression. 1,2 As our ability to 

treat depression has improved over the years, 

thanks in great part to advances in pharmacology 

and behavioral therapies, it is now critically important 

to recognize and treat this debilitating 

disease in individuals with cancer. 3 Evidence exists 

that untreated depression is associated with a 

worse overall survival for some cancer patients 

and, paradoxically, that up to half of patients 

with cancer and concurrent depression are undertreated 

or receive no treatment. 4–6 Medical 

Manuscript submitted August 2, 2010; accepted December 

20, 2010. 

Correspondence to: Alicia K. Morgans, MD, Massachusetts 

General Hospital, 55 Fruit Street, Boston, MA 02114; 

telephone: (617) 724-4000; fax: (617) 643-0798; e-mail: 

amorgans@partners.org 

J Support Oncol 2011;9:54–58 © 2011 Published by Elsevier Inc. 

doi:10.1016/j.suponc.2011.01.004 

oncologists receive little or no formal training in 

psycho-oncology yet are often faced with patients 

who exhibit changes in mood and become 

progressively disabled by psychiatric symptoms. 

Methodical assessment and frequent inquiry may 

identify patients with cancer and depression. 

Peeling Back the Onion: Sorting 

through Symptoms to Reach a 

Diagnosis 

A diagnosis of cancer often precipitates intense 

emotions such as fear, sadness, and sometimes 

anger. 2 Individuals who may never have 

given much thought to their own death are confronted 

with the very real possibility of a shortened 

life and future suffering. Roles and relationships 

shift, careers are interrupted, and daily 

routines may be sacrificed to make room for cancer 

treatment. Add to this the financial worries 

that often accompany a serious illness and it is 

not surprising that patients may require some 

level of professional guidance or intervention in 

order to cope with the crisis. As a quick rule of 

thumb, it takes about 3–4 weeks after diagnosis 

to adjust, and during that period it is quite normal 

for patients to experience intense feelings. 7 

Weissman and Worden, among the first psychiatrists 

to study distress in cancer patients, described 

an acute syndrome of distress over existential 

plight with the diagnosis and with a 

recurrence that lasts about 100 days. 8 Most individuals, 

given time and adequate support, will 

find the inner resources to cope with distressing 

symptoms and find a new normal. Not all do 

however, and it is important for oncologists to 

inquire at regular intervals about how the patient 

is feeling and coping with illness. A recent study 

by Lo et al 9 found that predictors of depressive 

symptoms in patients with solid tumors included 

younger age, antidepressant use at baseline, lower 

self-esteem and spiritual well-being, greater attachment 

anxiety, hopelessness, the physical 

burden of symptoms, and proximity to death. 


To facilitate screening for emotional distress in the 

context of a diagnosis of cancer, the National Comprehensive 

Cancer Network (NCCN) established guidelines that 

provide a reproducible algorithm for triaging patients with a 

suspected depression to mental health professionals. 10 These 

guidelines were updated in 2010 and are widely available. 11 

The consensus definition of distress in cancer is “a multifactorial, 

unpleasant emotional experience of a psychological 

(cognitive, behavioral, emotional), social, and/or spiritual nature 

that may interfere with the ability to cope effectively 

with cancer, its physical symptoms and its treatment. Distress 

extends along a continuum, ranging from common feelings of 

vulnerability, sadness, and fears to problems that can become 

disabling, such as depression, anxiety, panic, social isolation, 

and existential and spiritual crisis.” 10 By framing distress as a 

very broad concept, the guidelines separate the broad gamut 

of normal emotions from the distinct psychiatric syndromes of 

anxiety and depression which require specialized professional 

interventions. 12 

Distress may be a normal response to a threat or crisis, but 

depressive symptoms should alert the clinician that something 

more serious is going on. The appearance of persistent 

symptoms of dysphoria, hopelessness, helplessness, loss of selfesteem, 

feelings of worthlessness, and suicidal ideation indicates 

a psychiatric illness. 13 The DSM-IV defines a major 

depressive episode as experiencing either dysphoria or anhedonia 

in addition to at least five somatic symptoms for at least 

2 weeks. 14 These somatic symptoms may well overlap with 

those experienced by patients as a direct result of their cancer 

or its treatment. Among these are changes in appetite, 

weight, or sleep; fatigue; loss of energy; and a diminished 

ability to think or concentrate. The challenge for clinicians is 

to tease apart the physiologic consequences of disease and side 

effects of medications from those due to profound and disabling 

psychiatric syndromes. 

Many symptoms caused by cancer itself can be confused 

with neurovegetative symptoms of depression. Pain is known 

to modulate the reporting of symptoms; fatigue and weight 

changes are often secondary to cancer treatment or the illness 

itself. Patients often feel fatigued due to the heightened metabolic 

state present when there is a high burden of disease, 

and cytokines elevated in malignancy have been shown to 

cause fatigue and appetite suppression. There is a growing 

literature regarding the development of aberrant sleep patterns 

in patients with cancer, which can be mistaken for 

depressive daytime somnolence or insomnia. 15–18 Some cancers 

themselves are associated with a higher risk of depressive 

symptoms, including pancreatic cancer and cancers of the 

head and neck. 19–21 Chemotherapy can also induce fatigue, 

insomnia, and anhedonia, as can the steroids often used 

concomitantly with chemotherapeutic or biologic agents. Interferon-alpha, 

used to treat melanoma and renal cell cancer, 

has been associated with depression in 3%–40% of patients; 

and there is a 5% rate of suicidal thoughts. 22 

Cancer patients exhibit a range of coping styles and varying 

degrees of emotional resiliency. If a patient is able to 

Morgans and Schapira 

process his or her emotional responses to the physical threat 

of a diagnosis and becomes mobilized in such a way that he or 

she obtains useful information and is able to prioritize concerns, 

obtain social support, and move toward a coherent 

treatment plan, one can easily assume that he or she is coping 

well. 23 On the other hand, if the patient appears unable to 

make a decision about treatment, avoids addressing or discussing 

important issues, and retreats from family, friends, 

and/or the medical team, one can infer that he or she is 

having trouble coping and could benefit from a referral to a 

mental health professional for evaluation. 23 Known risk factors 

for poor coping and for developing depression include 

social isolation, use of few coping strategies, a history of 

recent losses or multiple obligations, inflexible coping strategies, 

the presence of pain, and socioeconomic pressures. 8,23 In 

extreme cases, patients may resort to deferring decisions or 

simply denying the problem. 

Keep in mind there may also be cultural or personal barriers 

that interfere with a timely and accurate diagnosis of 

depression. 12 Many families believe strongly in the “power of 

positive thinking” and need to feel that their family member 

is a “fighter.” This type of encouragement may at times be 

helpful for a patient, but it may not leave a safe opening for 

the expression of fear, pain, or depressed mood. If the matriarch 

or patriarch of the family has supported everyone else 

through the difficulties in their lives, she or he may not feel 

able to show weakness and seek help for depression. This can 

be a difficult patient to diagnose as the only clue to suffering 

may be easy to miss. In fact, if there are very few questions or 

complaints when there is clear physical suffering, one needs to 

worry that the patient is unable to express his or her deep 

concerns. The clinician who spots this situation early on may 

be able to lead the patient in the direction of expressing his 

or her feelings by suggesting that others in similar situations 

also experience stress or sadness. Finding a private time to 

talk, away from family members, may also provide a more 

comfortable environment for a candid conversation. 

If we think of the disease trajectory as a marathon, then we 

can learn to recognize certain landmarks along the course and 

remember that these pose enormous challenges to patients. In 

addition to receiving the initial diagnosis, the period of active 

treatment, the conclusion of active treatment, and the time of 

disease recurrence pose specific challenges and precipitate 

intense emotions. Disease recurrence is a time of great anxiety 

when there is a need to plan for future treatment and an 

upheaval of the timeline a patient may have made. 24 

Should the Oncologist Offer Treatment for 

Depression? 

Oncologists assume an important role in the medical care 

of their patients and often initiate or modify treatments for 

other medical conditions. If a patient develops hypertension 

or diabetes during or as a direct consequence of treatment, 

most oncologists feel comfortable starting medication and 

may then comanage the patient with internists. Primary care 

physicians and oncologists are typically familiar with a few 



basic antidepressants, and many are willing to prescribe these 

for patients who meet the diagnostic criteria for depression, 

especially since it takes weeks to achieve adequate therapeutic 

levels for many of these drugs. Recognizing the presence of 

depression is thus a key diagnostic intervention. 

Several efforts have been made to develop self-report 

screening inventories that can improve the accuracy and 

efficiency of detection of depressive symptoms and are brief 

enough to administer in the setting of an office visit. Some 

tools have been validated and correlate well with more detailed 

inventories, although the gold standard remains the 

detailed psychiatric interview. 25 A single-item interview 

screening proposed by Chochinov et al 25 years ago performs as 

well as or better than longer instruments and is remarkably 

simple to remember. Asking patients “Are you depressed?” in 

a brief screening interview correctly identified the eventual 

diagnostic outcome of every patient in initial studies and has 

been adopted broadly by oncologists and palliative care clinicians 

caring for patients who are terminally ill. 

We support immediate referral to a psychiatrist for any 

patient who exhibits symptoms of depression, and there is 

universal agreement that any person who may be suicidal 

should be referred immediately for urgent psychiatric evaluation. 

In practice, however, there are two main barriers to 

successful referrals for those who may be considered to be 

“managing” and not considered at risk for suicide: Patients are 

sometimes resistant to or reluctant to accept a recommendation 

for referral, and the shortage of mental health professionals 

trained in psycho-oncology limits quick access. It is, 

therefore, not surprising that cancer clinicians often initiate 

pharmacologic therapy for depression and provide emotional 

support to patients and families. Kadan-Lottick and colleagues 

5 reported that although 90% of patients agreed that 

they were willing to receive treatment for emotional distress 

associated with their cancer diagnosis, only 28% accessed 

treatment. Approximately 55% of the patients diagnosed in 

that study with major psychiatric disorders did not access 

treatment. It has been our experience that oncologists are 

often willing to initiate pharmacologic therapy while the 

patient is waiting for an appointment with a specialist. 

The most frequently prescribed antidepressant medications 

are the selective serotonin reuptake inhibitors (SSRIs). Frequently, 

the choice of antidepressant is based on the sideeffect 

profile of a particular medication as there are many 

effective options, none of which appears to be significantly 

more efficacious than the others. 7 Antidepressants considered 

to be sedating may not be the preferred option for patients 

who have significant neurovegetative symptoms including 

fatigue and low energy. Conversely, antidepressants that 

cause anorexia and insomnia are poor options for patients 

experiencing sleepless nights and continued weight loss. Options 

for more activating antidepressants include sertraline, 

escitalopram, bupropion, and venlafaxine, while more sedating 

antidepressant medications include paroxetine and mirtazapine. 

7 Methylphenidate, a drug frequently used to treat 

attention-deficit/hyperactivity disorder, has been very effec- 

tive in patients with low energy and anorexia. 26,27 Starting at 

a low dose in the morning, especially in the elderly, helps to 

minimize tachycardia and sleeplessness, which can be unwanted 

side effects of this medication. Lastly, a key point 

when choosing a medication is the potential for drug–drug 

interactions. Multiple antidepressants, including paroxetine, 

fluoxetine, fluvoxamine, and bupropion, interact with the 

cytochrome P-450 2D6 system, making them more likely to 

interact with medications commonly used in oncology. 28 One 

example of this potential for interaction occurs with tamoxifen, 

which is metabolized into its active form, endoxifen, by 

the cytochrome P-450 2D6 system. It may not be available in 

adequate concentrations in the setting of antidepressant medications 

like paroxetine, an inhibitor of cytochrome P-450 

2D6. Whether this ultimately influences the efficacy of anticancer 

treatment is still under investigation. 

While psychotherapy is outside the scope of most practicing 

oncologists, it may be helpful to provide patients with 

some guidance about the range of available therapies. Individuals 

may express a clear preference for nonpharmacologic 

treatments, so it is important for cancer clinicians to familiarize 

themselves with a few such options. These include 

cognitive behavioral therapy (CBT), intensive psychotherapy, 

and group therapy. These interventions can aid patients 

in reducing anxiety and in strengthening their personal coping 

mechanisms. Studies to rigorously evaluate the efficacy of 

these interventions have been challenging to complete because 

of the lack of a “gold standard” definition of depression 

in cancer, no consensus on an appropriate length of treatment, 

no clear way to monitor compliance with a given 

therapy, and varied definitions of appropriate end points. 12 

Despite the challenges, several meta-analyses have been compiled 

to sort through the data. The more commonly referenced 

meta-analyses have included thousands of patients undergoing 

nonpharmacologic interventions ranging from 

individual psychotherapy to group therapy as far back as 

1954. 29–34 None of the interventions indicate that any particular 

therapy is more clearly beneficial than another. 

CBT has received recent attention and appears to be a 

good option for many cancer patients with depression. A 

review by Williams and Dale in the British Journal of Cancer in 

2006 33 outlines 10 studies focusing on the use of CBT in 

cancer patients with mixed results. Of these, only two found 

CBT to be ineffective, whereas the rest demonstrated some 

benefit in reduction of depressive symptoms and improvement 

in quality of life for patients with a wide assortment of primary 

malignancies. Most found early improvement in symptoms 

but not necessarily long-term persistence of the initial positive 

effects. Group therapy has also been thoroughly studied 

in depression in cancer patients since Spiegel’s landmark 

study in the late 1980s and has been shown to decrease 

anxiety, depression, and pain and to increase effective coping. 

34–39 Many patients report positive experiences in support 

groups, but others express an intuitive fear that listening to 

other patients’ concerns and negative thoughts will impair 

their own overall mood and outlook. Not all patients feel 


comfortable expressing their personal fears, doubts, and frustrations 

with a group of relative strangers. Any of these 

concerns is a sufficient reason to advise more personalized 

attention in a private therapy session with a specialist. Choosing 

between individual psychotherapy, group, and family 

therapy can be construed as another aspect of providing truly 

“personalized” cancer care. 

A substantial number of patients worldwide turn to complementary 

and alternative therapies for the treatment of 

cancer and cancer-related symptoms. 40–42 Estimates of the 

prevalence of complementary and alternative therapy use 

vary widely due to differences in definitions and inaccuracies 

in self-reporting and patient selection. There are emerging 

data that up to 60%–80% of cancer patients avail themselves 

of some form of alternative therapy at some point in the 

trajectory of their disease. 42 This number varies widely, likely 

because the definition of “complementary and alternative 

therapies” is so broad and can include prayer, use of herbal 

medications, acupuncture, and meditation. In one study of 

early-stage breast cancer patients, the use of alternative medicine 

was significantly associated with patients experiencing 

depressive symptoms, heightened fear of recurrence, greater 

physical symptoms, and poor sexual satisfaction. 42 At 1 year, 

all patients, both those using complementary and alternative 

therapies and those using traditional methods of care, experienced 

an improvement in quality of life. 


1. Pirl WF. Evidence report on the occurrence, 

assessment, and treatment of depression 

in cancer patients. J Natl Cancer Inst Monogr 

2004;32:32–39. 

2. McDaniel JS, Musselman DL, Porter MR, 

Reed DA, Nemeroff CB. Depression in patients 

with cancer: diagnosis, biology, and treatment. 

Arch Gen Psychiatry 1995;52:89–99. 

3. Trijsburg RW, Van Knippenberg FCE, Rijpma 

SE. Effects of psychological treatments on 

cancer patients: a critical review. Psychosom 

Med 1992;54:489–517. 

4. Gruneir A, Smith TF, Hirdes J, Cameron R. 

Depression in patients with advanced illness: an 

examination of Ontario complex continuing care 

using the minimum data set 2.0. Palliat Support 

Care 2005;3:99–105. 

5. Kadan-Lottick NS, Vanderwerker LC, Block 

SD, Zhang B, Prigerson HG. Psychiatric disorders 

and mental health service use in patients 

with advanced cancer: a report from the Coping 

with Cancer Study. Cancer 2005;104:2872– 

2881. 

6. Steel JL, Geller DA, Gamblin TC, Olek MC, 

Carr BI. Depression, immunity, and survival in 

patients with hepatobiliary carcinoma. J Clin Oncol 

2007;25:4526–4527. 

7. Pirl W. Depression, anxiety, and fatigue. In: 

Chabner B, Lynch J, Longo D, eds. Harrison’s 

Manual of Oncology. New York: McGraw-Hill; 

2008:190–196. 

8. Weissman A, Worden J. The existential 

plight in cancer: significance of the first 100 

days. Psychiatr Med 1976;7:1–15. 

For patients who do not meet the criteria for clinical depression 

and have no interest in or access to support groups, it is 

worth remembering there are other interventions that can facilitate 

adjustment and diminish symptoms of anxiety. Expressive 

writing, music, or art therapy and other activity-based therapies 

may provide the necessary vehicles for self-expression. 

Conclusion 

Depression clearly affects patients with cancer, and establishing 

the depression diagnosis is the first step toward progress 

in treatment. Despite the challenges, diagnosis is possible 

by establishing that the symptoms of depression are negatively 

impacting patients’ abilities to cope with their circumstances 

and maintain balance in their lives. It is critical not only to 

make the diagnosis of depression but also to strongly encourage 

patients to seek treatment, either through pharmacologic 

or nonpharmacologic means. While we make every effort to 

eradicate our patients’ malignancies, we owe it to them to 

work just as diligently to improve their daily lives by treating 

associated depression. 

Acknowledgments: We thank Dr. Donna Greenberg and Dr. 

William Pirl for their thoughtful review of this manuscript 

and helpful comments. 


9. Lo C, Zimmermann C, Rydall A, et al. Longitudinal 

study of depressive symptoms in patients 

with metastatic gastrointestinal and lung 

cancer. J Clin Oncol 2010;28(18):3084–3089. 

10. National Comprehensive Cancer Network. 

NCCN practice guidelines for the management 

of psychosocial distress. Oncology (Williston 

Park) 1999;13:113–147. 

11. National Comprehensive Cancer Network. 

NCCN clinical practice guidelines in oncology. 

Distress management. V.1.2010. Available 

at www.nccn.org. 

12. Fisch M. Treatment of depression in cancer. 

J Natl Cancer Inst Monogr 2004;32:105–111. 

13. Mermelstein HT, Lesko L. Depression in 

patients with cancer. Psychooncology 1992;1: 

199–215. 

14. American Psychiatric Association. Diagnostic 

and Statistical Manual of Mental Disorders, 

4th ed. Washington DC: American Psychiatric 

Association; 1994. 

15. Chen ML, Yu CT, Yang CH. Sleep disturbances 

and quality of life in lung cancer patients 

undergoing chemotherapy. Lung Cancer 2008; 

62:391–400. 

16. Savard J, Simard S, Blanchet J, et al. Prevalence, 

clinical characteristics, and risk factors 

for insomnia in the context of breast cancer. 

Sleep 2001;24:583–590. 

17. Savard J, Morin CM. Insomnia in the context 

of cancer: a review of a neglected problem. 

J Clin Oncol 2001;19:895–908. 

18. Palesh OG, Roscoe JA, Mustian KM, Roth 

T, Savard J, Ancoli-Israel S, Heckler C, Purnell JQ, 

Morgans and Schapira 

Janelsins MC, Morrow GR. Prevalence, demographics, 

and psychological associations of 

sleep disruption in patients with cancer: University 

of Rochester Cancer Center–Community 

Clinical Oncology Program. J Clin Oncol 2010;28: 

292–298. 

19. Fras I, Litin EM, Pearson JS. Comparison 

of psychiatric symptoms in carcinoma of the 

pancreas with those in some other intraabdominal 

neoplasms. Am J Psychiatry 1967;123: 

1553–1562. 

20. Joffe RT, Rubinow DR, Denicoff KD, Maher 

M, Sindelar WF. Depression and carcinoma of the 

pancreas. Gen Hosp Psychiatry 1986;8:241–245. 

21. Morton RP, Davies ADM, Baker J, Baker 

GA, Stell PM. Quality of life in treated head and 

neck cancer patients: a preliminary report. Clin 

Otolaryngol 1984;9:181–185. 

22. Micromedex 2.0. DrugPoint summary, 

interferon alfa-2b. Available at http://www. 

micromedex.com/2 Accessed July 1, 2010. 

23. Spiegel D. A 43-year-old woman coping 

with cancer. JAMA 1999;281(4):371–377. 

24. Cella DF, Mahon SM, Donovan MI. Cancer 

recurrence as a traumatic event. Behav Med 

1990;16:15–22. 

25. Chochinov HM, Wilson KG, Enns M, 

Lander S. “Are you depressed?” Screening for 

depression in the terminally ill. Am J Psychiatry 

1997;154:674–676. 

26. Bruera E, Driver L, Barnes E, et al. Patient 

controlled methylphenidate for cancer-related 

fatigue: a preliminary report. Proc Annu Meet 

Am Soc Clin Oncol 2003;22:737. 



27. Homsi J, Nelson KA, Sarhill N, et al. A 

phase II study of methylphenidate for depression 

in advanced cancer. Am J Hosp Palliat Care 

2001;18:403–407. 

28. Kalash GR. Psychotropic drug metabolism 

in the cancer patient: clinical aspects of management 

of potential drug interactions. Psychooncology 

1998;7:307–320. 

29. Devine EC, Westlake SK. The effects of 

psychoeducational care provided by adults with 

cancer: meta-analysis of 116 studies. Oncol Nurs 

Forum 1995;22:1369–1381. 

30. Meyer TJ, Mark MM. Effects of psychosocial 

interventions with adult cancer patients: 

a meta-analysis of randomized experiments. 

Health Psychol 1995;14:101–108. 

31. Newell SA, Sanson-Fisher RW, Savolainen 

NJ. Systematic review of psychological therapies 

for cancer patients: overview and recommendations 

for the future. J Natl Cancer Inst 2002;94: 

558–584. 

32. Sheard T, Maguire P. The effect of psychological 

interventions on anxiety and depres- 

sion in cancer patients; results of two metaanalyses. 

Br J Cancer 1999;80:1770–1780. 

33. Williams S, Dale J. The effectiveness of 

treatment for depression/depressive symptoms 

in adults with cancer: a systematic review. Br J 

Cancer 2006;94:372–390. 

34. Spiegel D, Bloom JR, Kraemer HC, Gottheil 

E. Effect of psychosocial treatment on survival 

of patients with metastatic breast cancer. 

Lancet 1989;2:888–891. 

35. Berkman LF, Syme SL. Social networks, 

host resistence, and mortality: a nine year follow-up 

study of Alameda County residents. Am J 

Epidemiol 1979;109:186–204. 

36. Funch DP, Marshall J. The role of stress, 

social support and age in survival from breast 

cancer. J Psychosom Res 1983;27:77–83. 

37. Ganster DC, Victor B. The impact of social 

support on mental and physical health. Br J Med 

Psychol 1988;61:17–36. 

38. Fawzy FI, Cousins N, Fawzy NW, Kemeny 

ME, Elashoff R, Morton D. A structured psychiatric 

intervention for cancer patients: I. Changes 

over time in methods of coping and affective 

disturbance. Arch Gen Psychiatry 1990;47:720– 

725. 

39. Spiegel D, Bloom JR. Group therapy 

and hypnosis reduce metastatic breast carcinoma 

pain. Psychosom Med 1983;45:333– 

339. 

40. Gansler T, Kaw C, Crammer C, Smith T. A 

population-based study of prevalence of complementary 

methods use by cancer survivors: a 

report from the American Cancer Society’s studies 

of cancer survivors. Cancer 2008;113:1048– 

1057. 

41. Richardson MA, Sanders T, Palmer JL, Greisinger 

A, Singletary SE. Complementary/alternative 

medicine use in a comprehensive cancer 

center and the implications for oncology. J Clin 

Oncol 2000;18(13):2505–2514. 

42. Burstein HJ, Gelber S, Guadagnoli E, 

Weeks JC. Use of alternative medicine by 

women with early-stage breast cancer. N Engl 

J Med 1999;340(22):1733–1739. 


O R I G I N A L R E S E A R C H 

Evaluating the “Good Death” Concept 

from Iranian Bereaved Family Members’ 

Perspective 

Sedigheh Iranmanesh, PhD, Habibollah Hosseini, doctoral student, and 

Mohammad Esmaili, MSc student 

Alife-threatening disease such as cancer involves 

patients and their families. Even if 

people today prefer to die at home and to 

be cared for by their family members, they still 

need professional services and support. 1 Improving 

the quality of death has become a major need 

for patients, their families and loved ones, as well 

as health-care professionals, researchers, and policy 

makers who organize and provide care. 2 Since 

the 1960s, our approach to this need has been 

palliative care. The philosophy of end-of-life care 

is to alleviate suffering and to improve the quality 

of life of patients who are facing death. Despite 

a recent increase in the attention given to 

improving end-of-life care, our understanding of 

what constitutes a good death is surprisingly 

lacking. The Longman Dictionary of Contemporary 

English 3 defines good death as “the calm end of life 

of a person without any worry or excitement.” 

Family members who face the death of their 

loved ones are key to evaluating the good death 

concept. Their views on death could be used by 

the health-care system to evaluate the quality of 

end-of-life care. Therefore, the concept of a 

“good death” as perceived by the general Iranian 

population could be sought by studying the views 

of a representative sample of bereaved family 

members. Health-care providers, who are aware 

of what constitutes a good death, have an openness 

and flexibility when working with dying 

From the Razi Faculty of Nursing and Midwifery, Kerman 

Medical University, Kerman, Iran. 

Manuscript Submitted August 4, 2010; Accepted December 

1, 2010. 

Correspondence to: Habibollah Hosseini, Razi Faculty of 

Nursing and Midwifery, Kerman Medical University, Kerman, 

Iran; Phone: 00983413205220; Fax: 00983413205218; 

e-mail: seha.hosseini@gmail.com 


doi:10.1016/j.suponc.2010.12.003 

Abstract Improving end-of-life care demands that first you define 

what constitutes a good death for different cultures. This study was 

conducted to evaluate a good death concept from the Iranian bereaved 

family members’ perspective. A descriptive, cross-sectional study was 

designed using a Good Death Inventory (GDI) questionnaire to evaluate 

150 bereaved family members. Data were analyzed by SPSS. Based on 

the results, the highest scores belonged to the domains “being respected 

as an individual,” “natural death,” “religious and spiritual comfort,” 

and “control over the future.” The domain perceived by family 

members as less important was “unawareness of death.” Providing a 

good death requires professional caregivers to be sensitive and pay 

attention to the preferences of each unique person’s perceptions. In 

order to implement holistic care, caregivers must be aware of patients’ 

spiritual needs. Establishing a specific unit in a hospital and individually 

treating each patient as a valued family member could be the best way 

to improve the quality of end-of-life care that is missing in Iran. 

patients to improve quality of care as well as the 

patient’s quality of life. 

From a review of different studies, the core 

quality of a good death varies among cultures. In 

a qualitative study, Griggs 4 analyzed perceptions 

of a “good death” among community nurses in 

England. Nurses identified several key themes for 

a good death, such as: symptom control, patient 

choice, honesty, spirituality, interprofessional relationships, 

effective preparation, organization, 

and provision of seamless care. American researchers 

concluded that a good death involves 

respect for the individual’s autonomy with open 

communication among family members. 5 Vig 

and Pearlman 6 also reported that “good death” 

has an individual meaning for Americans and 

does not have a consensual meaning. In Ghana, 

Van der Greest 7 found that a good death is integrated 

with a peaceful death, meaning peace 

with others, being at peace with one’s own life 

and soul, dying in the fullness of time, dying at 



home, and being surrounded by relatives. For the Japanese, 

Hattori et al. 8 found that a good death is a multidimensional, 

individual experience based on personal and sociocultural 

domains of life that incorporate the person’s past, present, and 

future. In Norway, Ruland and Moore 9 conducted research on 

the theory of a peaceful end of life which has five major 

concepts: not being in pain, experience of comfort, experience 

of dignity/respect, being at peace, and closeness to significant 

others/persons who care. In Thailand, people commonly 

used “peaceful death” instead of “good death.” 

Kongsuwan and Locsin 10 reported that Thai intensive care 

unit nurses perceived peaceful death as awareness of dying, 

creating a caring environment, and promoting end-of-life 

care. In Muslim society, Tayeb et al 11 identified three domains 

related to a good death: religion and faith, self-esteem 

and personal image, and satisfaction about family security. 

After reviewing these studies, we determined there is no 

universal definition of good death and it is based on sociocultural 

context. The subject of death and dying has a religious 

and sociocultural background, yet Iranian health-care providers 

mainly depend on Western references. Moreover, upon 

reviewing the literature in Iran, no published study related to 

defining the concept of “good death” was located. This descriptive 

study was thus designed to determine what constitutes 

a good death in the Iranian context. 

Context 

Iran is one of the most ancient world civilizations and part 

of the Middle East culture. The population is approximately 

67 million, and of this 51% is less than 20 years old and 6.5% 

is 65 or older. 12 The majority (99.4%) of the people in Iran 

consider themselves as religious, 13 and religious beliefs 

strongly and explicitly deal with death. 14 

Iranians are familiar with death. Besides the Iran–Iraq war 

and natural disasters in recent years, the major causes (65%) 

of death among Iranians are heart disease, cancer, and accidents. 

15 Apart from chronic disease, accidents seem to be a 

significant cause of death among Iranian people. In Iran, the 

overall national curriculum for registered nursing education 

includes just a few hours of academic education about death. 

End-of-life care remains a new topic in the Iranian healthcare 

system. Hospice care units, which are common in Western 

countries, are not available in Iran. 

Most religions are represented in this country; however, 

Islam is the most prevalent. Sareming 16 indicates that Muslims 

are taught that Allah gives birth and death. Allah determines 

the appointed term for every human. Only Allah 

knows when, where, and how a person will die. For a Muslim, 

death is the transition from the earthly form of existence 

to the next. 17 Tayeb et al 11 explained that Muslims prefer to 

approach death with a certainty that someone is there to 

prompt them with the Shahadah, reciting a chapter of the 

Quran, dying in a position facing Mecca, and dying in a holy 

place such as a mosque. 

Method 

DESIGN 

There was approval from the heads of hospitals prior to the 

collection of data. The study employed a descriptive design 

and was conducted in two hospitals that had oncology units 

in southeast Iran. 

PARTICIPANTS 

Referring to the hospitals’ and patients’ documents, 150 

bereaved family members of patients who died within 1 year 

were identified. They were called by the researcher and asked 

to participate in this study. 

BACKGROUND INFORMATION 

At first, a questionnaire was designed in order to obtain 

background information which was assumed to influence the 

good death concept. It included questions about gender, age, 

marital status, previous studies about death, and level of 

education. 

INSTRUMENTS 

The good death concept was evaluated using the Good 

Death Inventory (GDI). The GDI was designed by Miyashita 

et al 18 for evaluating a good death from the bereaved 

family members’ perspective. This scale has 51 

items. The items are graded from 1 to 7 (1 � strongly 

disagree to 7 � strongly agree). A factor analysis made by 

Miyashita et al 18 on research made in a Japanese setting 

revealed that the questions could be divided into 18 domains: 

(1) physical and psychological comfort, (2) dying in 

a favorite place, (3) good relationship with medical staff, 

(4) maintaining hope and pleasure, (5) not being a burden 

to others, (6) good relationship with family, (7) physical 

and cognitive control, (8) environmental comfort, (9) being 

respected as an individual, (10) life completion, (11) 

natural death, (12) preparation for death, (13) role accomplishment 

and contributing to others, (14) unawareness of 

death, (15) fighting against cancer, (16) pride and beauty, 

(17) control over the future, and (18) religious and spiritual 

comfort. 

For translation from English into Farsi, the standard 

forward–backward procedure was applied. Translation of 

the items and the response categories was independently 

performed by two professional translators, and then temporary 

versions were provided. Afterward they were backtranslated 

into English, and after a careful cultural adaptation 

the final versions were provided. Translated 

questionnaires went through pilot testing. Suggestions by 

family members were combined into the final versions. 

Reliability and validity. The translated scale was originally 

developed and tested in a Japanese cultural context, which 

is different from the research contexts, so the validity and 

reliability of both scales were rechecked. A factor analysis 

(rotated component matrix) on the results was done in 

order to examine the context validity of the GDI. The 


Table 1 

Some GDI Subdomain Scores 

SCALE SUBSCALES MEAN/SD 

Good Death 

Inventory 

Being respected as an individual 6.55/0.69 

Not being treated as an object or 

a child 

6.33/0.63 

Being respected for one’s values 6.45/0.65 

Natural death 6.36/0.52 

Not being connected to medical 

instruments or tubes 

6.15/0.57 

Not receiving excessive treatment 6.24/0.47 

Religious and spiritual comfort 

Patient felt that he or she was 

protected by a higher power 

Having family support 

6.02/0.52 

5.67/0.68 

Patient was supported by religion 5.87/0.55 

Control over the future 6.55/0.65 

Knowing how long one will live 6. 50/0.54 

Knowing what to expect about 

one’s condition in the future 

6.43/0.58 

Unawareness of death Dying 

3.05/0.72 

without awareness that one is 

dying 

2.84/0.66 

Living as usual without thinking 

about death 

2.95/0.74 

concession of the items was similar to the Japanese results, 

and 18 components were identified. The validity of the 

scale was assessed through a content validity discussion. 

Scholars of statistics and nursing care have reviewed the 

content of the scale from religious and cultural aspects of 

death and agreed upon a reasonable content validity. To 

reassess the reliability of the translated scale, alpha coefficients 

of internal consistency and 3-week test–retest coefficients 

(n � 30) of stability were computed. The alpha 

coefficient for GDI was 0.68. The 3-week test–retest coefficient 

of stability for the GDI was 0.79. Therefore, the 

translated scale presented an acceptable reliability. 

DATA COLLECTION AND ANALYSIS 

Accompanied by a letter including some information 

about the aim of the study, the questionnaires were handed 

out by the second author to 150 family members who were 

introduced by the matron of two hospitals over 2 months 

(May/June 2010) in southeast Iran. Some oral information 

about the study was also given by the third author. Participation 

in the study was voluntary and anonymous. We 

distributed 150 sets of questionnaires. In all collected data, 

98% of all questions were answered. Data from the questionnaires 

were analyzed using the Statistical Package for 

Social Scientists (SPSS, Inc., Chicago, IL). A Kolmogorov-Smirnov 

test indicated that the data were sampled 

from a population with normal distribution. Descriptive 

statistics of the sample and measures that were computed 

included frequencies, means, and reliability. Cross-table 

Table 2 

Correlation between GDI Domains and Demographic 

Factors 

SCALE SUBSCALE AGE LEVEL OF EDUCATION 

Good Death 

Inventory 

analysis (Spearman’s test) was used to examine relationships 

among demographic factors and scores on the GDI. 

Results 

Being respected 

as an individual 

r � 0.325 

P � 0.001 

Beauty and pride r � 0.274 

P � 0.01 

Good relationship 

with family 

Unawareness of 

death 

r � 0.293 

P � 0.002 

Iranmanesh et al 

r � 0.344 

P � 0.000 

r � 0.259 

P � 0.04 

r � –0.315 

P � 0.003 

PARTICIPANTS 

A descriptive analysis of the background information 

revealed that the participants belonged to the age group of 

16–68 years, with a mean age of 33 years, and were mainly 

female (81%). About 68% were married, and the majority 

had an academic degree. Regarding personal study about 

death, 36.9% had read some things about death previously. 

FINDINGS 

Descriptive analysis indicated that the highest scores belonged 

to the domains “being respected as an individual” 

(mean � 6.55), “natural death” (mean � 6.36), “religious and 

spiritual comfort” (mean � 6.02), and “control over the 

future” (mean � 6.55) (Table 1). 

The domains and the components perceived as important 

by bereaved family members were (1) physical and 

psychological comfort, (2) dying in a favorite place, (3) 

maintaining hope and pleasure, (5) not being a burden to 

others, (6) good relationship with family, (7) physical and 

cognitive control, (8) environmental comfort, and (9) life 

completion. The domain perceived by family members as 

less important was “unawareness of death” (mean � 3.05). 

Significant differences were found between some domains 

of a good death and demographic characters of 

family members. Older participants were more likely to 

perceive a good death as “being respected as an individual” 

and “having good relationships with family members.” 

Among participants, those who had a higher level of education 

were more likely to view a good death as “being 

respected as an individual” and “pride and beauty.” There 

was a negative correlation between level of education and 

“unawareness of death” (Table 2). 

Discussion 

According to the factor analysis, 18 domains contributing 

to a good death were identified. However, the domains of the 

“good death” concept that were perceived as important by 



bereaved family members were similar to those in Japan. This 

finding thus indicates that these perceptions are foundational 

elements of a good death, regardless of ethnicity or cultural 

differences. 

The results indicated that most family members are 

likely to view a good death as “being respected as an 

individual” and having “control over the future.” According 

to Murata, 19 approaching death can cause a sense that 

life is meaningless and a loss of the patient’s well-being 

founded on temporality, relationships, and autonomy. Providing 

a good death means that dying patients are able and 

allowed to participate in the same human interactions that 

are important throughout life and appreciating patients as 

unique and “whole persons,” not only as “diseases” or 

cases. 20 It means supporting patients’ well-being through 

positive stimulation, for example, offering beautiful views 

and tasty meals. 21 A good death is also perceived by family 

members as “religious and spiritual comfort.” Ghavamzadeh 

and Bahar 14 claimed that among Iranians religious 

beliefs strongly and explicitly deal with the fact of death. 

This finding reflects the result of Tayeb et al, 11 who found 

that Muslims believe that death is closely linked to faith. 

They appreciated the importance of access to any needed 

spiritual or emotional support. Steinhauser et al. 20 also 

found that 89% of American patients and 85% of their 

families emphasize that a good death is “being at peace 

with God” and “prayer.” 

Participants perceived a good death as a “natural death.” 

Johnson et al 22 claimed that death without “machines,” 

“tubes,” and “lines” is considered more dignified and aesthetically 

pleasing. Withdrawal or withholding of treatment 

of the highly invasive and technological sort is conceptualized 

as restoring patient dignity and, to a small 

degree, personhood. 22 Many deaths were not considered 

“good” because of inherent problems within a culture of 

care that usually strives to prolong life and prevent death. 23 

Similarly, Miyashita et al 18 reported that most Japanese 

view unnecessary life-prolonging treatments such as vasopressors, 

antibiotics, and artificial hydration as barriers to 

achieving a good death. The domain perceived by family 

members as less important was “unawareness of death.” 

This is consistent with Steinhauser et al’s 20 finding that 

96% of American patients emphasized “knowing what to 

expect about one’s physical condition” achieves a good 

death. This is inconsistent with Tang et al’s 24 claims that 

in many traditional cultures (eg, most Asian countries and 

a few European cultures), in an effort to protect the patient 

from despair and a feeling of hopelessness, family caregivers 

often exclude patients from the process of information 

exchange. This is also in contrast to Miyashita et al’s 18,25 

findings, where many Japanese do not want to know the 

seriousness of their condition. Our findings could be explained 

by the other results of this study. The results 

indicated that the majority of participants had a high level 

of education. The other findings showed there is a negative 

correlation between level of education and “unawareness of 

death.” Since the majority of participants were well-educated, 

it can be concluded that they were less likely to view 

a good death as “unawareness of death.” This has also been 

found by Montazeri et al. 26 

The results showed that the family members’ age was 

correlated with some aspects of a good death. Miyashita et 

al 18 also found that the older the family member, the more 

positively he or she would look on the patient’s death. 

They claimed that death at younger ages tended to be 

evaluated as a bad death. This could be explained by their 

earlier study, where they found that age and psychosocial 

maturity inversely related to death anxiety. 27 Based on the 

results, level of education positively influenced some domains 

of a good death. There was a negative correlation 

between level of education and “unawareness of death,” 

with Montazeri et al 26 finding that Iranian patients with a 

low level of education were more likely to not know the 

diagnosis. 

Conclusion 

According to the results of this study, providing a good 

death requires professional caregivers to be sensitive and 

pay attention to the preferences of each unique person’s 

perceptions through her or his senses. This includes views, 

tastes, sounds, smells, and bodily contact. The ability of a 

dying person to see a sunset may seem petty but is important 

in providing high-quality care for people at the end of 

their lives. The same goes for the other senses. These 

circumstances deserve attention in all educational programs 

and especially in programs dealing with end-of-life 

care. In order to implement holistic care, caregivers must 

pay attention to patients’ spiritual needs. Establishing a 

specific palliative care unit in a hospital and meeting each 

patient as a unique being and part of a family could be the 

best way to improve the quality of end-of-life care that is 

missing in Iran. It requires cultural preparation and public 

education through the media and by well-educated staff. 

Since demographic variables influenced the evaluation of a 

good death from the bereaved family members’ perspective, 

public education needs different strategies. 

LIMITATION 

All data in this study were collected by use of self-report 

questionnaires. The dependence on self-report aspects in 

this study may have caused an overestimation of some of 

the findings due to variance, which is common in different 

methods. The respondents were predominantly female, 

which limits the generalization of the results for male 

respondents. Moreover, the convenience sample of Iranian 

bereaved family members, which is not representative of 

the entire Iranian population, could weaken the generalization 

of the findings. Further research is necessary to 

illuminate the concept of a good death as perceived by the 

general Iranian population. 


References 

1. Proot IM, Abu-Saad HH, Crebolder HFJM, 

Luker KA, Widdershoven GAM. Vulnerability of 

family caregivers in terminal palliative care at 

home; balancing between burden and capacity. 

Scand J Caring Sci 2003;17:113–121. 

2. Patrick DL, Engelberg RA, Curtis JR. Evaluating 

the quality of dying and death. J Pain 

Symptom Manage 2001;22:717–726. 

3. Longman Dictionary of Contemporary 

English. Canada: Pearson Education; 2003. 

4. Griggs CH. Community nurses’ perceptions 

of a good death: a qualitative exploratory study. 

Int J Palliat Nurs 2010;16:140–149. 

5. Winland-Brown JE. Public’s perceptions of 

a good death and assisted suicide. Issues Interdisc 

Care 2001;3:137–144. 

6. Vig EK, Pearlman RA. Good and bad dying 

from the perspective of terminally ill men. Arch 

Intern Med 2004;164:977–981. 

7. Van der Greest S. Dying peacefully: considering 

good death and bad death in Kwahu-Tafo, 

Ghana. Soc Sci Med 2004;58:899–911. 

8. Hattori K, McCubbin MA, Ishida DN. Concept 

analysis of good death in the Japanese 

community. J Nurs Scholarsh 2006;38:165–170. 

9. Ruland CM, Moore SM. Theory construction 

based on standards of care: a proposed 

theory of the peaceful end of life. Nurs Outlook 

1998;46:169–175. 

10. Kongsuwan W, Locsin RC. Promoting 

peaceful death in the intensive care unit in Thailand. 

Int Nurs Rev 2009;56:116–122. 

11. Tayeb MA, Al-zamel E, Fareed MM, 

Abouellail HA. A ”good death”: perspectives of 

Muslim patients and health care providers. Ann 

Saudi Med 2010;30:215–221. 

12. WHO. www.who.Int/countries/en/#s, 2008. 

13. European Values Study Group and World 

Values Surveys Association. European and World 

Values Surveys, four-wave integrated data file, 

1981–2004. http://www.worldvaluessurvey.org/ 

services/index.html, September, 2007. 

14. Ghavamzadeh A, Bahar B. Communication 

with the cancer patients in Iran, information 

and truth. Ann N Y Acad Sci 1997;809:261–265. 

15. Cancer Institute of Iran. www.cancerinstitute.ac.ir. 

Access date is June 2010. 

16. Sareming N. Islamic teaching on death 

and practices toward the dying person. J Soc Sci 

Hum 1997;3:75–91. 

17. Sheikh A. Death and dying. A Muslim perspective. 

J R Soc Med 1998;91:138–140. 

18. Miyashita M, Morita T, Sato K, Hirai K, 

Shima Y, Uchitomi Y. Good Death Inventory: a 

measure for evaluating good death from the 

bereaved family members’ perspective. J Pain 

Symptom Manage 2008;35:486–498. 

19. Murata H. Spiritual pain and its care in 

patients with terminal cancer: construction of a 

conceptual framework by philosophical approach. 

Palliat Support Care 2003;1:15–21. 

20. Steinhauser KE, Clipp EC, McNeilly M, 

Christakis NA, McIntyre LM, Tulsky JA. In search of 

a good death: observations of patients, families, 

and providers. Ann Intern Med 2000;132:825–832. 

Iranmanesh et al 

21. Iranmanesh S, Haggstrom T, Axelsson K, 

Savenstedt S. Swedish nurses’ experiences of 

caring for dying people: a holistic approach. Holist 

Nurs Pract 2009;23:243–252. 

22. Johnson N, Cook D, Giacomini M, Willms 

D. Towards a good death: end of life narratives 

constructed in an intensive care unit. Cult Med 

Psychiatry 2000;24:275–295. 

23. Beckstrand RL, Callister LC, Kirchhoff KT. 

Providing a ”good death”: critical care nurses’ 

suggestions for improving end-of-life care. Am J 

Crit Care 2006;15:38–45. 

24. Tang ST, Liu TW, Lai MS, Liu LN, Chen 

CH, Koong SL. Congruence of knowledge, experiences, 

and preferences for disclosure of 

diagnosis and prognosis between terminallyill 

cancer patients and their family caregivers 

in Taiwan. Cancer Invest 2006;24:360– 

366. 

25. Miyashita M, Sanjo M, Morita K, Uchitomi Y. 

Good death in cancer care: a nationwide quantitative 

study. Ann Oncol 2007;18:1090– 

1097. 

26. Montazeri A, Tavoli A, Mohagheghi MA, 

Roshan R, Tavoli Z. Disclosure of cancer diagnosis 

and quality of life in cancer patients: 

should it be the same everywhere. BMC Cancer 

2009;9:1–21. 

27. Russac RJ, Gatliff C, Reece M, Spottswood 

D. Death anxiety across the adult years: an examination 

of age and gender effects. J Death 

Stud 2007;31:549–561. 



Symptom Experience in Patients with 

Gynecological Cancers: The 

Development of Symptom Clusters 

through Patient Narratives 

Violeta Lopez, RN, PhD, Gina Copp, RN, PhD, Lisa Brunton, RN, MSN, and 

Alexander Molassiotis, RN, PhD 

Being diagnosed with gynecological cancer 

is associated with high distress levels, particularly 

in younger patients and in advanced 

disease. 1 The residual effects of surgery 

and various treatments are known to have a 

profound and long-lasting impact on quality-oflife 

issues with significant and potentially detrimental 

change to women’s self-esteem, mental 

health, sexual functioning, and fertility. 2 Although 

recent medical advances have increased 

survival rates, few investigations have been conducted 

to examine the interplay of physical and 

psychological symptoms on this group of patients. 

Moreover, previous studies have focused 

predominantly on gathering quantitative data 

such as the frequencies and types of symptoms, 

with little or no information about the gynecological 

cancer patients’ symptom experiences. 2,3 

This knowledge is important to gain if we are to 

understand the quality-of-life and supportive 

care issues that affect this group of patients. 

Thus, our current knowledge about gynecological 

cancer patients’ experiences from a qualitative 

perspective remains limited. 

From the Research Centre for Nursing and Midwifery Practice, 

Australian National University, Medical School, Canberra, 

Australia; School of Health and Social Sciences, 

Middlesex University, London; School of Nursing, Midwifery 

and Social Work, University of Manchester, Manchester, 

United Kingdom. 

Manuscript submitted February 22, 2010; accepted December 

13, 2011. 

Correspondence to: Violeta Lopez, TCH, RCNMP, Building 

6, Level 3, East Wing, Yamba Drive, Garran, 2605, Canberra, 

Australian Capital Territory (ACT), Australia; telephone: 

�612 6244 2333; fax: �612 6244 2573; e-mail: violeta. 

lopez@anu.edu.au; violeta.lopez@act.gov.au 


doi:10.1016/j.suponc.2011.01.005 

Abstract The vast majority of the increasing cancer literature on physical 

and psychological symptom clusters is quantitative, attempting 

either to model clusters through statistical techniques or to test priori 

clusters for their strength of relationship. Narrative symptom clusters 

can be particularly sensitive outcomes that can generate conceptually 

meaningful hypotheses for symptom cluster research. We conducted a 

study to explore the explanation of patients about the development 

and coexistence of symptoms and how patients attempted to selfmanage 

them. We collected 12-month qualitative longitudinal data 

over four assessment points consisting of 39 interview data sets from 10 

participants with gynecological cancer. Participants’ experiences highlighted 

the presence of physical and psychological symptom clusters, 

complicating the patients’ symptom experience that often lasted 1 year. 

While some complementary and self-management approaches were 

used to manage symptoms, few options and interventions were discussed. 

The cancer care team may be able to develop strategies for a 

more thorough patient assessment of symptoms reported as the most 

bothersome and patient-centered sensitive interventions that encompass 

the physiological, psychological, sociocultural, and behavioral 

components of the symptom experience essential for effective symptom 

management. 

The physical effects on women after being 

diagnosed with gynecological cancer are often 

attributed not only to the symptoms arising 

from the disease itself but, most importantly, 

from the side effects of treatment such as surgery, 

chemotherapy, and radiotherapy. 3–5 

Symptoms such as fatigue, frequency of urination, 

bleeding, weight loss, and ascites are 

commonly experienced by patients, particularly 

those with ovarian cancers. 6 Once diagnosed, 

gynecological cancer patients often go 

on to face a prolonged course of treatments 

which contribute to further symptoms such as 

chemotherapy-induced alopecia, 7 dermatolog- 


ical toxicity, 8 fatigue, sleep disturbance, 9 nausea, vomiting, 

and sexual dysfunction. 10 Portenoy et al. 11 reported that 

ovarian cancer patients alone experienced a mean of 10.2 

symptoms with a range of 0–25 concurrent symptoms. 

Similarly, 13.4 concurrent symptoms were reported in a 

study of 49 women undergoing chemotherapy, which 

caused disruption to the patients’ quality of life. 6 

The psychological state of patients with gynecological 

cancers has also been investigated, particularly in association 

with increased risks of psychological morbidity such as 

anxiety and depression. 2 In a longitudinal study of women 

with ovarian cancer, Gonçalves et al. 12 found that neuroticism 

was associated with persistent psychological morbidity 

and suggested the need for routine and regular psychological 

screening for cancer patients. Newly diagnosed 

women with gynecological cancer also appeared to experience 

diverse psychological symptomatology that persisted 

over the first 6 weeks after the diagnosis. 2 

The relationship between symptom experience, distress 

produced, and quality of life has also been pursued, of 

particular interest being the direct correlation between 

improvement of symptoms and increased quality of life. 

Ferrell et al. 3 found that ovarian cancer patients not only 

experienced distress but often differently ordered the importance 

of symptoms at different phases of their illness. 

They also found that these patients utilized resourcefulness 

and innovative ideas to manage their symptoms. These 

authors suggested that symptom experience may be associated 

with, and can be mediated by, the influence of variables 

such as disease state, demographic and clinical characteristics, 

or individual and psychological factors. 3 It is 

therefore unsurprising that treatment-induced symptoms 

have been a major concern of most studies to gather information 

about symptoms arising from residual treatment 

or disease progression as well as frequency and types of 

symptoms. 5 To date, longitudinal studies have yet to be 

undertaken to gather information prospectively about gynecological 

cancer patients’ symptom experiences. Consequently, 

the patients’ personal experiences of physical and 

psychological symptoms, such as their concerns, perceptions, 

and responses to symptoms, remain largely unexplored. 

Such information is important in the development 

of interventions for symptom management and the provision 

of supportive care. Also, while some literature exists in 

relation to ovarian cancer symptoms, minimal related work 

has focused on other types of gynecological cancer, suggesting 

a gap in the literature. 

The aim of our study was to explore the physical and 

psychological symptom experience in patients with gynecological 

cancer undergoing radiotherapy and/or chemotherapy 

over the first year from diagnosis. Specific objectives of the 

study were to (1) qualitatively assess the possible relationships 

among symptoms resulting from cancer treatments in patients 

with gynecological cancer, as understood by patients, and (2) 

explore how patients with gynecological cancer manage the 

symptoms they experience. 

Lopez et al 

Methods 

A descriptive qualitative longitudinal design using face-toface 

interviews was used in this study. Qualitative descriptive 

methods serve to provide descriptions of facts about a phenomenon. 

13 Sandelowski 14 elucidates that qualitative descriptive 

research methods lend themselves to the data to 

produce comprehensively and accurately detailed summaries 

of different participants’ experiences of the same event. Interviews 

were conducted by an experienced qualitative researcher. 

Interviews were conducted prospectively over four 

time periods: beginning of treatment (T1) and three (T2), six 

(T3), and 12 months (T4) later. This time frame was chosen 

as these are the critical times over which patients with cancer 

most commonly experience symptoms as a result of treatments 

or disease progression. 15 Leventhal and Johnson’s 16 

self-regulation theory was used as the study’s theoretical 

framework, assisting us in developing the interview guide 

around symptom identification, exploration of meaning and 

consequence, and attempts to control or manage it. Their 

self-regulation theory suggests that symptoms activate a cognitive 

search process, which results in the construction or 

elaboration of illness representation. These representations 

then serve as standards against which new information is 

matched and evaluated. Comparisons of current sensations 

with cognitive representations allow for interpretation of new 

symptoms and for evaluation of the seriousness of current 

symptoms. Hence, fear behaviors (distress) or instrumental 

behaviors (coping) are the result of simultaneous parallel 

psychophysiological processes in response to the threatening 

experience. The response may be different from individual to 

individual, based on past experience and the cognitive processes 

involved, as may the strategies used to cope with the 

experience. Dodd et al 17 simplified the symptom experience 

as including an individual’s perception of a symptom, evaluation 

of the meaning of a symptom, and response to a 

symptom. 

After approval from the ethics committee, patients were 

recruited from a large specialist oncology center in the UK a 

few weeks after diagnosis and prior to commencement of 

adjuvant treatment. Patients were provided with information 

about the study, and written consent was obtained. Ten 

patients were recruited from a list of consecutive newly diagnosed 

patients through purposeful sampling, and five declined 

participation, primarily due to the long-term commitment 

necessary for the study and being too upset with the diagnosis. 

Maximum variation was used 13 to capture core experiences 

and central, shared aspects or impacts of having a gynecological 

cancer rather than confining to specific aspects of different 

types of gynecological cancer. The sample included patients 

with any type of gynecological cancer and those 

receiving chemotherapy and/or radiotherapy. Patients with 

cognitive impairment, metastasis with central nervous system 

involvement, or life expectancy of less than 6 months at 

recruitment or who were unable to carry out the interview 

were excluded. Patients initially were provided with brief 

information from their oncologist; upon showing an interest, 


Symptom Experience in Patients with Gynecological Cancers 

potential participants were provided with a detailed information 

sheet and had a discussion with the research nurse. Upon 

agreement, patients signed a consent form and the first interview 

was scheduled. Participants were followed up for one 

year. Past experience, judgment on the quality of the data 

obtained, and data saturation were the key determinants in 

the decision to have a sample size of 10 over four times (�40 

possible transcripts) with the possibility of recruiting more if 

data were not saturated with the initial sample, although in 

our study this did not need to take place. 

An interview guide was used, starting with a broad question, 

such as “How have you been feeling physically this last 

week?” This was followed by questions relating to the psychological 

symptoms participants experienced, how these related 

to their physical symptom experience, what they thought 

when a symptom occurred, what impact the symptoms had on 

their life, and how they managed the symptoms. New issues 

identified in the early interviews were incorporated into the 

interview guide for subsequent interviews. Each interview 

lasted about an hour to an hour and a half. Interviews were 

conducted in the patients’ homes. Information about sociodemographic 

characteristics including age, education, and marital 

status was obtained from patients, who completed an 

initial sociodemographic form. Disease- and treatment-related 

information (diagnosis, treatment received, stage of cancer) 

was obtained from the patients’ medical notes. Interviews 

were recorded and transcribed verbatim. 

Data were analyzed line by line using content analysis to 

code the content of each interview and to map major categories. 

Categories were compared by two of the researchers, 

the project lead investigator and another independent person. 

The analyzed categories were compared and discussed until 

agreement was reached. Symptoms that were expressed in T1 

were grouped together if more than two participants spontaneously 

mentioned an association between at least two of the 

symptoms. In T2–T4 we continued this process, focusing 

primarily on changes in the initial cluster. Symptoms were 

grouped together as patients discussed them, and if patients 

reported the same symptom in different contexts, this was 

coded separately. No participant was asked for specific symptoms 

as the questions in the interview were broad to allow for 

important aspects of the symptom experience in each woman 

and each interview to surface. A final consensus was sought 

after comparisons and discussions for all categories. 13 

Credibility of the qualitative data was maintained by ensuring 

voluntary participation. Analyzed data were constantly 

discussed and checked by two independent persons, which 

acted as a constant peer-review process to ensure the analyzed 

data were true findings and free from potential bias. All 

interviews were audiotaped, and participants’ verbatim quotes 

were provided to represent categories and subcategories identified, 

which further ensured reliability by reducing the risk of 

selective data filtering by the investigators through recall or 

summation. Consistency was maintained by comparing initial 

categories within and across the data gathered from the participants 

to ensure repeatability of the categories. Field notes 

were reviewed as a kind of inquiry audit to prevent potential 

bias and to ensure the stability of data. 

Results 

PATIENT CHARACTERISTICS 

All 10 participants completed the interviews at the four 

time points, over the course of one year. One interview 

transcript (at T3) was subsequently found to be unusable, due 

to tape recorder malfunction, and was excluded from the 

analysis, thus leaving a total of 39 data sets of interviews over 

four time points. The mean age of the group was 62.8 years 

(SD � 7.7, range 51–72). Most were married (n � 6), two 

were separated, and two were widowed. The majority (n � 7) 

had secondary/high school education. Six were retired, two 

were homemakers, and two reported technical/manual work. 

Half of the participants (5/10) had ovarian cancer, while the 

rest had uterine (1/10), cervical (2/10), or endometrial (1/10) 

cancer and one had both uterine and cervical cancer. Seven 

participants had surgery. Of the 10 participants, six had chemotherapy, 

three had radiotherapy, and one had chemotherapy 

followed by radiotherapy. Chemotherapy included carboplatin 

(n � 2), carboplatin and paclitaxel (Taxol) (n � 5), 

and cisplatin (n � 1). Half the patients were at an early 

disease stage (stage 1 or 2, n � 5), three were at stage 3, one 

was at stage 4, and the stage was unknown in one patient. 

QUALITATIVE DATA 

Patients identified symptoms in an interlinked manner 

rather than in isolation, suggesting some symptom clustering. 

There was always a key symptom mentioned together with 

several others that were reported as co-occurring or resulting 

symptoms. These associations and explanations helped patients 

to make sense of the symptoms and rationalize or 

legitimize the complexity of the symptom relationships and 

the difficulty in having control over the symptoms. Participants 

gave meaning to the physical symptoms experienced 

alongside psychological responses and how they managed to 

alleviate them. The meaning element was fairly stable across 

times as women discussed primarily the occurrence of symptoms, 

their impact on their lives, and their struggle to cope 

with them. What was an evident change in the perception, 

however, for most symptoms was the frustration from the 

“chronicity” of symptoms and the differential impact of symptoms 

at different times in their disease trajectory. Symptoms 

were described as co-occurring with one influencing others, 

giving an understanding of the formation of symptom clusters. 

Four major narrative symptom clusters emerged from the data. 

Tiredness, sleeplessness, pain, depression, and weakness. The 

most common symptom experienced by all patients that persisted 

over the 12-month period was tiredness, which was also 

related with sleep disturbance associated with pain, tingling 

sensation of the hands and feet, and anxiety. Tiredness was 

experienced throughout the year as recounted by several participants: 


“Basically, it’s after the effects of the treatment that I was 

feeling tired cos I’m having radiotherapy every day and 

chemotherapy once a week.” (GYP 01 at T1) 

“Just me body felt tired, me body felt tired of it [radiotherapy]. 

I felt rotten. I couldn’t do anything. It’s depressing.” 

(GYP 12 at T2) 

“Physically, I’m alright. I felt alright except I get tired easily, 

but other than that, I feel alright.” (GYP06 at T3) 

“What I do find is that it’s depressing to feel physically tired 

all the time and I don’t know why.” (GYP04 at T4) 

Difficulties with sleep were also associated with depression. 

For some (4/10) participants, feelings of depression occurred 

as they went through the treatment, as two participants described: 

“I’ve actually felt quite depressed this last week and actually 

burst into tears, which is something I haven’t done before. 

But it was sort of overwhelming—these symptoms.” 

(GYP08 at T3) 

“These symptoms are affecting my sleep. If I can’t sleep, I 

get tired and I can’t do anything the next day, then I get 

depressed.” (GYP06 at T3) 

Depression was often the result of uncertainty and fear. 

Half of the participants expressed feelings of uncertainty lasting 

until the twelfth month, as highlighted by two of them: 

“It’s very uncertain you know. It’s a very uncertain way of 

life. You don’t know ...like I go and see ...I go back every 

three months for check-up. You’re living your life for checkups. 

So every four months you’re thinking ...‘Is everything 

alright?’ You live your life for those four months’ check-ups. 

The frightening bit is the uncertainty of it and it’s depressing 

just thinking of what will happen next.” (GYP04 at T3) 

“I can’t sleep thinking about what will happen all the time. 

The uncertainty keeps you awake.” (GYP012 at T3) 

However, this feeling seemed to subside by T4 as they became 

more in control and able to cope by not dwelling on it and 

just accepting the disease, its treatment, and symptoms. This 

marked shift in coping styles in T4 characterized the patients’ 

increased positive response to symptoms at this stage. 

When participants were tired, they also complained of 

weakness. The expressions used included “muscle tone” and 

“muscle strength” weakness. However, a slight improvement 

in this symptom was observed from T3 onward. Tiredness and 

its related symptoms affected the participants’ ability to get on 

with usual routine housework; however, support from husbands, 

family, and friends helped the participants to cope 

with their symptoms: 

“I’ve got such a strong group of friends and relatives and they 

all live around me ...so all the family are around me and 

they’ll all come at least once a day.” (GYP11 at T4) 

Very few management options were discussed. For both 

tiredness and weakness, participants used a variety of selfmanagement 

approaches such as taking a rest even for half an 

Lopez et al 

hour each day or doing some physical fitness like walking and 

pilates from the third month onward. To get their muscle 

tone back to normal, some participants expressed their desire 

to “get fit,” so they walked or went cycling. Two participants 

reported taking herbal medicines, such as echinacea, after 

surgery and before chemotherapy. They perceived this as 

helpful in building up their immune system. Pain was managed 

by taking painkillers, as prescribed by their doctors. 

Praying was also reported as a strategy used to combat the 

anxieties related to the illness. 

Hair loss, ocular changes, body image, identity experience, and 

anxiety. Hair loss, including body hair such as eyebrows and 

eyelashes, was reported by four participants, all of whom 

had received chemotherapy. In the beginning, participants 

did not want to wear wigs and were anxious that it portrayed 

a symbol to others that they had cancer: 

“I do not want my family to see me without hair as they will 

know I have cancer and then they will start to worry about 

me.” (GYP10 at T1) 

Later on, they no longer cared whether they wore a wig or 

not, especially when they themselves and others accepted the 

situation. Such an experience was described as follows: 

“I noticed my hair falling and got me a wig the first day.” 

(GYP11 at T1) 

“I noticed that ... the hairs in my nose, I think have all 

disappeared as well.” (GYP10 at T2) 

“My hair came back like Shirley Temple, curly but it all 

came back slate gray and I didn’t like it.” (GYP11 at T4) 

On a couple of occasions, negative feelings were externalized 

through talking about someone else, often a famous 

person. This may have facilitated the expression of difficult 

emotions. Such a transference is depicted below: 

“My hair is more or less gone but this time my husband just 

shaved it all off. I bung my hat on and that’s it. I feel sorry 

for young girls. It must be horrendous cos I think of Kylie 

[Minogue, pop singer], for someone like her to lose her hair 

must have been terrible.” (GYP03 at T3) 

Although participants understood that hair loss was an 

expected and common consequence of chemotherapy, its impact 

in some patients was more difficult to accept. Losing hair 

was seen as a realization that they had cancer or increased 

one’s identity as a cancer patient. The same patient also 

talked about hair loss and anxiety: 

“I think one of the most difficult things is that you might be 

feeling alright physically and then you’ve got a bald head. 

When I put my wig on, I feel alright. But no hair is a big 

thing—even though people say you looked alright without it, 

you don’t.” (GYP03 at T3) 

In some participants, hair loss, especially eyelashes, was 

connected with blurred vision as they believed that eyelashes 

protected their eyes, as reported by one (1/10) participant at 

T2 and three (3/9) participants at T3, around the end of their 

treatment. 



“My eyes seem as though they’re a bit blurred. I feel like I 

need eyeglasses. It’s a thing that annoyed me most. It just felt 

like there’s a film on your eyes. I wonder if it’s something to 

do with hair loss.” (GYP10 at T2) 

One participant was worried about her blurred vision being 

associated with other health problems: 

“I’ve had slightly sort of, not blurred vision but zig-zaggy 

vision that made me a bit ...‘Oh dear,’ you know ...have 

I got a brain tumor? ...a strange sort of thing that upsets my 

balance.” (GYP08 at T3) 

Gastrointestinal problems: nausea, loss of appetite, taste 

changes, bowel function, weight changes, and distress. Nausea, 

appetite changes, and changes in bowel function (diarrhea or 

constipation) were the most common symptoms reported in 

relation to gastrointestinal system problems that distressed 

patients. Patients who reported these occurrences received 

either chemotherapy or radiotherapy, and one received both. 

However, these symptoms were of limited extent as women 

reported them as mild or of less importance and perceived 

them as more manageable. Nausea was only reported by one 

participant at T1 and was relieved by prescribed medications. 

Another participant reported loss of taste throughout the year 

of the study. Loss of appetite was reported by one participant 

at T2 and T3. Weight loss, which three of the participants 

attributed to diarrhea, was also reported: 

“My tummy’s a bit off, had a bit of diarrhea, but that’s the 

norm.” (GYP05 at T1) 

“I lost weight but then again, I don’t know if that’s down to 

eating then going to the toilet.” (GYP04 at T3) 

However, weight gain was reported by six participants at T3 

and five participants at T4, which was the key distressing 

nutritional problem described, although for some it was seen 

in a more positive way: 

“I put on weight since the radium. But it’s a small price to 

pay isn’t it? A bit of weight for all that you’ve gone through.” 

(GYP15 at T4) 

Numbness and tingling sensations in the hands and feet, restlessness, 

sleeplessness, and depression. A common physical 

problem experienced by three of the participants who all 

received chemotherapy was tingling sensations of the hands 

and feet, which increased over time. At T4, three out of 10 

participants still experienced numbness and tingling sensations 

as described by one participant: 

“I was worst after my last treatment, all sorts, my feet, my 

fingers were really bad ... always tingly. My feet they’re 

numb and I get cramps. It’s weird, they get too cold. It’s 

depressing especially if I can’t sleep.” (GYP10 at T3) 

“I still got funny toes and fingers. They feel fat and podgy. 

It’s depressing. It’s difficult to explain, it feels like because 

they’re not dead but ...I know I’ve got them, if that makes 

any sense. I find it difficult to spread them.” (GYP10 at T4) 

Participants sometimes related the sensation to achy joint 

pains, as if they were getting the flu. This sensation also made 

them feel restless at night, contributing to sleeplessness. One 

participant related this to the side effect of paclitaxel (Taxol); 

therefore, her medication was changed to liposomal doxorubicin. 

Two participants tried to self-manage the feeling of 

coldness and numbness of their feet and fingers by soaking 

them in hot water. For those participants who experienced 

sleeplessness, due to feelings of numbness and tingling sensations 

in the hands and feet, wearing bed socks or soaking 

them in warm water, as well as using reiki and massage, were 

the management strategies described. 

Discussion 

This study explored the explanations of patients about the 

development and coexistence of symptoms and how patients 

attempted to self-manage them. This is one of the few studies in 

the literature, and the only one in gynecological cancer, which 

has explored clusters of symptoms in a narrative manner. Its 

longitudinal nature, unusual in qualitative research due to the 

inherent issues in the analysis of such data, was another strength 

of the study as it allowed us to explore shifts in the symptom 

experience, perception, and meaning over time (although meaning 

was fairly stable and participants talked little about it). The 

vast majority of the increasing literature on symptom clusters is 

quantitative, attempting either to model clusters through statistical 

techniques or to test priori clusters for their strength of 

relationship. However, such clusters may be biased, not only 

from the technique used but also from the content of self-reports 

utilized to collect the data. The narrative symptom clusters could 

rectify problems with statistical measures as they reflect the 

unique patient experience in the patients’ own words and can 

assist in the development of (patient-centered rather than statistically 

based) symptom clusters that can then be tested quantitatively 

with larger samples. Hence, narrative symptom clusters 

can be particularly sensitive outcomes and can generate conceptually 

meaningful hypotheses for symptom cluster research. 

Key symptoms experienced by the participants were tiredness, 

pain, body image changes, gastrointestinal changes, and 

peripheral neuropathy associated with chemotherapy, which 

concur with past studies of primarily ovarian cancer patients. 

9,10 Out of the four clusters identified, one is applicable 

to all patients irrespective of treatment and two are clearly 

linked with chemotherapy. Symptoms varied in intensity but 

tended to subside in a year’s time for the majority of patients. 

Acceptance brought about self-management strategies to 

overcome both the physical and psychological effects of cancer 

and its treatment, but most important was the support 

they received from families and friends. In addition, the fact 

that some symptoms decreased over time may be due to some 

symptoms being linked to the time since the end of treatment; 

such symptoms could have naturally resolved after completion 

of treatment. However, we have limited information on 

the natural history of symptoms in patients with different 

types of gynecological cancer. 


As with other studies, the most common symptom experienced 

by these participants was tiredness, 3,9,18 often associated 

with sleep disturbance due to pain, peripheral neuropathy, 

change in bowel function, and depression. Because these women 

complained of tiredness throughout the year, having social support 

from their husbands, families, friends, and neighbors helped 

them carry on with their usual household roles. This highlighted 

the important role caregivers play in supporting patients with 

cancer. Participants clearly differentiated between the symptom 

of tiredness/fatigue (a complex symptom involving physical, 

mental, and motivational aspects) from weakness (which is related 

more to muscle strength). This differentiation is evident in 

the literature, 19 and while they may be related symptoms, they 

should be assessed separately as they may necessitate different 

management strategies. Dodd et al 20 have shown the clustering of 

the symptoms of fatigue, sleep disturbance, pain, and depression 

in breast cancer patients, similarly to the work of Liu et al. 21 This 

quantitative work and our narrative cluster strongly support the 

existence and clinical relevance of this symptom cluster. 

Loss of weight in the beginning was due to gastrointestinal 

disturbance including nausea, loss of taste, and change in bowel 

function. These findings concur with the literature, where such 

symptoms are prevalent up to one year posttreatment. 22 However, 

as time passed, the participants regained their weight, often 

above their prediagnosis level. Such a gastrointestinal symptom 

cluster has also been supported in the quantitative literature on 

symptom clusters, although the relevant items within the cluster 

very much depend on the items included in the data-collection 

scale. 23 Our own work with 143 patients over one year (n � 504 

symptom assessments) has also identified a gastrointestinal cluster, 

with the key symptom being weight loss, together with loss 

of appetite and difficulties swallowing, experienced by up to 

one-quarter of a heterogeneous sample of cancer patients at the 

one-year time point. 24 The attempts highlighted by the majority 

of participants to control their weight suggest that this is an 

important issue for these women. The inability to control weight 

may be frustrating and a key stressor in women with gynecological 

cancer. This assertion needs further investigation as the 

information we have about this topic to date derives almost 

exclusively from breast cancer patients. Weight control may be 

an important component of survivorship in these women, and it 

should be incorporated in the follow-up care of patients beyond 

breast cancer. 25 While the use of medication was mentioned 

with regard to the presence of gastrointestinal symptoms, no 

interventions have taken place with taste changes and other 

nutritional concerns. Interventions around the experience and 

enjoyment of eating and food should be an important research 

focus in the future, as should work around weight gain, for which 

we currently have limited information. 

Concern about hair loss was mentioned by only four participants. 

They were concerned mainly that it identified them 

to others as a cancer patient as baldness became the main 

element of the cancer patients’ everyday life and identity. 7 

Participants were not concerned about their self-image but 

rather more concerned about protecting others (particularly 

family) and being treated differently. For these participants, 

Lopez et al 

they accepted that loss of hair was a side effect of treatment 

and viewed regrowth of hair as a positive effect, which concurred 

with the study by Sun et al. 10 Ocular changes reported 

by three participants (out of 10) during treatment and up to 

six months later are an underreported issue in the literature. 

This is despite the established association between some types 

of chemotherapy (ie, cyclophosphamide, cisplatin) and ocular 

changes. More focus should be directed to this area, as well as 

to identifying reversible and irreversible ocular changes in the 

survivorship period. The narrative clustering of symptoms 

such as hair loss and ocular changes (connected with being 

“visible” cancer patients) with body image, identity, and anxiety 

is an interesting clustering of physical and psychological 

interrelated symptoms, with body image being the key symptom 

in this cluster. Our past work has highlighted the relationship 

between body image changes and “disliking” self in 

up to 20% of the sample, although these did not cluster 

together after the six-month assessment point (end of treatments) 

and were most visible during the chemotherapy 

period. 24 With the exception of using wigs, patients did not 

mention using any interventions regarding the multiple symptoms 

experienced within this symptom cluster, suggesting that 

this is an important area of research in the future. 

Many of the physical symptoms reported were interrelated 

with descriptions of depression, uncertainty, body image, and 

identity as a cancer patient. While causal relationships cannot 

be ascertained from such a qualitative design, it is evident 

that there is a close relationship between the presence of 

physical symptoms, psychological status, and the impact 

of them in life. This is confirmed in a systematic review of 

studies with ovarian cancer patients 26 as well as other studies 

that support the association of symptoms of fatigue, pain, 

anxiety, and depression with quality of life. 27 A better understanding 

of these relationships is paramount in symptommanagement 

efforts, particularly as it is recognized that interventions 

need to be multimodal and to target more than 

one concurrent symptom, a clear message that comes from the 

symptom cluster research. 28 For the majority of the participants, 

when symptoms were not managed well, they experienced 

psychological responses such as depression, commonly 

seen in the literature. Participants in our study accepted that 

they would experience these symptoms, although their occurrence 

and intensity differed from patient to patient. Some 

participants were able to tolerate treatment with little physical 

discomfort, while in others symptoms prevented them 

from resuming usual functional activities and social roles, thus 

leading to feelings of depression. It would be interesting to 

identify in future research the factors that allow some patients 

to live well with cancer, moving away from the current 

research model of ill-health to a model of “wellness.” 

Furthermore, a key distressing symptom that was associated 

with impairments in a variety of life areas was peripheral 

neuropathy in patients receiving chemotherapy. This cluster 

has some similarities with the tiredness cluster (i.e. the, presence 

of sleep difficulties or depression), but women talked 

about it as a separate experience from tiredness. Peripheral 



neuropathy is a difficult symptom to manage in practice and 

may necessitate dose reductions or chemotherapy discontinuation; 

therefore, the development of management strategies 

for this symptom is imperative. Women complained of sleeping 

difficulties when they experienced numbness and tingling 

sensations in their hands and feet, and it was a frustrating 

symptom that was also associated with depression. This is 

another symptom cluster that merits further research, and it is 

only emerging in the literature. Our past work on symptom 

clusters has also identified the presence of a “hand–foot” 

symptom cluster, which consistently increased over time and 

suggested a chronic nature, although not all symptoms identified 

in the present study were part of the latter quantitative 

evaluation. 24 

It was surprising to see the minimal range of interventions 

used to manage symptoms, both self-management and formal 

ones directed by the clinicians. The latter had minimal presence 

in the women’s descriptions, with the exception of pain 

management, antiemetics, and antidiarrheal medication. Specific 

physical responses to treatment were dealt with by 

changing the type of chemotherapy and resorting to the use of 

herbal medicine for symptom management, such as echinacea 

and red clover, or other simple self-management techniques, 

such as soaking the feet in warm water, eating healthy food, 

and carrying out regular exercise. The use of complementary 

therapies was also found in the study by Ferrell et al, 3 complementing 

the medical care provided to help control the 

symptoms. 6 However, all of these attempts seemed to be ad 

hoc, with no clear understanding of processes and possible 

outcomes or any guidance about their use from health-care 

professionals. Possible reasons for this ad hoc and unsatisfactory 

underutilization of symptom-management interventions 

may include the “acceptance” by patients that some symptoms 

are part of their treatment, the British cultural norm of 

not complaining, limited confidence from both clinicians and 

patients on nonpharmacological interventions with variable 

quality of evidence of effectiveness, distance from patients’ 

homes to the specialist service to provide supportive care, 

limited understanding from the clinicians of the impact of 

symptoms on patients’ lives, and clinician time constraints. 

Although we purposely included women with a range of 

gynecological cancer diagnoses in order to gain an understanding 

of broadly applicable issues related to the physical 

and psychological symptom experiences of patients, the small 


1. Johnson RL, Gold MA, Wyche KF. Distress 

in women with gynecologic cancer. Psychooncology 

2010;19:665–668. 

2. Petersen RW, Graham G, Quinlivan JA. Psychological 

changes after a gynaecologic cancer. 

J Obstet Gynecol Res 2006;31:152–157. 

3. Ferrell B, Smith S, Cullinane C, Melancon C. 

Symptom concerns of women with ovarian cancer. 

J Pain Symptom Manage 2003;25:528–538. 

4. Hipkins J, Whitworth M, Tarrier N, Jayson 

G. Social support, anxiety and depression after 

chemotherapy for ovarian cancer: a prospective 

sample size limits the generalizability of the results. These 

symptom clusters will need further evaluation with statistical 

modeling. Also, the existence of symptom clusters in radiotherapy 

may not have surfaced well in our study with the 

inclusion of only three patients receiving radiotherapy, and 

this needs to be explored in future research. The length of 

treatment for each patient was variable, and knowledge of this 

would have enhanced the interpretability of the findings; 

however, this information was not available to us. Finally, 

although we wanted to focus on treatment-related symptoms, 

some of the symptoms (ie, anxiety and depression) may have 

multiple possible etiologies; and this needs to be considered in 

the interpretation of the findings. 

Conclusion 

Our study provides information on symptom experiences 

from the patients’ perspective, which could lead to a better 

understanding of how patients perceive, assess, monitor, and 

manage their symptoms. This is particularly useful as the 

majority of the symptom literature focuses on the experience 

of patients with ovarian cancer. This is also important background 

information in developing strategies or interventions 

that are patient-centered and sensitive to the needs of patients 

that has relevance to the current policy framework. It 

also highlights the need for a more thorough patient assessment, 

to assess which symptoms are most bothersome and 

how symptoms are interrelated, and has implications for the 

physiological, psychological, sociocultural, and behavioral 

components of the symptom experience essential for effective 

symptom management. While we have identified the presence 

and experience of some well-documented symptoms, we 

have also highlighted areas of importance for patients and 

some underreported symptoms that merit further research in 

the future. Narrative symptom clusters, such as those identified 

in the present study, can provide a stronger conceptual 

basis in the symptom cluster modeling work and can assist in 

identifying patient-relevant and clinically meaningful groups 

of symptoms that can be the focus of future cluster research. 

Acknowledgments: Funding for this study, as part of a 

program grant on patient experiences of cancer symptoms, 

was obtained from the Christie Hospital Charitable Fund. 


study. Br J Health Psychol 2004;9(pt 4):569– 

581. 

5. Williams PD, Piamjariyakul U, Ducey KA, 

Badura J, Boltz D, Olberding K, Wingate A, Williams 

AR. Cancer treatment, symptom monitoring, 

and self-care in adults. Cancer Nurs 2006;29: 

347–355. 

6. Donovan HS, Hartenbach EM, Method MW. 

Patient–provider communication and perceived 

control for women experiencing multiple symptoms 

associated with ovarian cancer. Gynecol 

Oncol 2005;99:404–411. 

7. Rosman S. Cancer and stigma: experience 

of patients with chemotherapy-induced 

alopecia. Patient Educ Couns 2004;52:333– 

339. 

8. Hackbarth M, Haas N, Fotopoulou C, Lichtenegger 

W, Schouli J. Chemotherapy-induced 

dermatological toxicity: frequencies and impact 

on quality of life in women’s cancer: results of a 

prospective study. Support Care Cancer 2008;16: 

267–273. 

9. Kayl AE, Meyers CA. Side-effects of chemotherapy 

and quality of life in ovarian and breast 


cancer patients. Curr Opin Obstet Gynecol 2006; 

18:24–28. 

10. Sun CC, Bodurka DC, Weaver CB, Rasu R, 

Wolf JK, Beyers MW, Smith JA, Wharton JT. Rankings 

and symptom assessments of side effects 

from chemotherapy: insights from experiences 

patients with ovarian cancer. Support Care Cancer 

2005;13:219–227. 

11. Portenoy RK, Thaler HT, Kornblith AB, Mc- 

Carthy-Lepore J, Friedlander-Klar H, Coyle N, 

Smart-Curley T, Kemeny N, Norton L, Hoskins W, 

Scher H. Symptom prevalence, characteristics 

and distress in a cancer population. Qual Life 

Res 1994;3:183–189. 

12. Gonçalves V, Jayson G, Tarrier N. A longitudinal 

investigation of psychological morbidity 

in patients with ovarian cancer. Br J Cancer 

2008;99:1794–1801. 

13. Patton MQ. Qualitative Evaluation and 

Research Methods, 2nd ed. Newbury Park, CA: 

Sage Publications; 1990. 

14. Sandelowski M. Real qualitative researchers 

do not count: the use of numbers in qualitative 

research. Res Nurs Health 2001;24:230– 

240. 

15. Hwang SS, Chang VT, Fairclough DL, Cogswell 

J, Kasimis B. Longitudinal quality of life in 

advanced cancer patients: pilot study results 

from a VA Medical Cancer Center. J Pain Symptom 

Manage 2003;25:225–235. 

16. Leventhal H, Johnson JE. Laboratory and 

field experimentation: development of a theory 

of self-regulation. In: Wooldridge JP, Schmitt 

MH, Skipper JK, Leonard RC, eds. Behavioral Science 

and Nursing Theory. St. Louis: Mosby; 1983: 

189–264. 

17. Dodd M, Janson S, Facione N, Faucett J, 

Froelicher ES, et al. Advancing the science of 

symptom management. J Adv Nurs 2001;33: 

668–676. 

18. Sun CC, Bodurka DC, Donato ML, Rubenstein 

EB, Borden CL, Basen-Engquist K, Munsell 

MS, Kavanagh JJ, Gershenson DM. Patient preferences 

regarding side effects of chemotherapy 

for ovarian cancer: do they change over time? 

Gynecol Oncol 2002;87:118–128. 

19. Teunissen SC, Wesker W, Kruitwagen C, 

de Haes HC, Voest EE, de Graeff A. Symptom 

prevalence in patients with incurable cancer: a 

systematic review. J Pain Symptom Manage 

2007;34:94–104. 

20. Dodd MJ, Cho MH, Cooper BA, Miaskowski 

C. The effect of symptom clusters on 

functional status and quality of life in women 

with breast cancer. Eur J Oncol Nurs 2010;14: 

101–110. 

21. Liu L, Fiorentino L, Natarajan L, Parker BA, 

Mills PJ, Sadler GR, Dimsdale JE, Rissling M, He F, 

Ancoli-Israel S. Pre-treatment symptom cluster 

in breast cancer patients is associated with 

worse sleep, fatigue and depression during chemotherapy. 

Psychooncology 2009;18:187–194. 

22. Tong H, Isenring E, Yates P. The prevalence 

of nutrition impact symptoms and their 

Lopez et al 

relationship to quality of life and clinical outcomes 

in medical oncology patients. Support 

Care Cancer 2009;17:83–90. 

23. Kim HJ, Barsevick AM, Tulman L, McDermott 

PA. Treatment-related symptom clusters in 

breast cancer: a secondary analysis. J Pain Symptom 

Manage 2008;36:468–479. 

24. Molassiotis A, Wengstrom Y, Kearney N. 

Symptom cluster patterns during the first year 

from the diagnosis with cancer. J Pain Symptom 

Manage 2010;39:847–858. 

25. Muraca L, Leung D, Clark A, Beduz MA, 

Goodwin P. Breast cancer survivors: taking 

charge of lifestyle choices after treatment. Eur 

J Oncol Nurs 2010; doi:10.1016/e.ejon.2009. 

12.001. 

26. Arden-Close E, Gidron Y, Moss-Morris R. 

Psychological distress and its correlates in ovarian 

cancer: a systematic review. Psychooncology 

2008;17:1061–1072. 

27. So WKW, Marsh G, Ling WM, Leung FY, 

Lo JCK, Yeung M, Li GKH. The symptom cluster 

of fatigue, pain anxiety, and depression and 

the effect on the quality of life of women 

receiving treatment for breast cancer: a multicentre 

study. Oncol Nurs Forum 2009;36: 

E205–E214. 

28. Dodd MJ, Miaskowski C, Paul SM. Symptom 

clusters and their effect on functional status 

of patients with cancer. Oncol Nurs Forum 2001; 

28:465–470. 



Estimating Minimally Important 

Differences for the Worst Pain Rating of 

the Brief Pain Inventory–Short Form 

Susan D. Mathias, MPH, Ross D. Crosby, PhD, Yi Qian, PhD, Qi Jiang, PhD, Roger Dansey, MD, and 

Karen Chung, PharmD, MS 

Certain outcomes, such as pain, are only 

known to patients and therefore are best 

reported through a patient-reported outcome 

(PRO) measure. To be clinically useful, a 

PRO measure must be valid, reliable, and responsive 

to change. In addition, interpretation of data 

from PRO measures is aided by estimation of the 

minimally important difference (MID). The 

MID is the smallest difference in a PRO measure 

that a patient would consider beneficial or detrimental. 

Although the MID may not affect the 

patient’s clinical treatment or care, patients are 

the primary stakeholders in the evaluation of 

PROs, and patient-perceived differences are particularly 

relevant in advanced stages of disease 

where palliation may be the focus of treatment. 1 

The MID may be estimated through distribution-based 

methods and/or anchor-based methods. 

Distribution-based methods are based on the 

distribution of the data. Examples of distributionbased 

methods include effect size measures, the 

standard error of measurement (SEM), one-half 

times the standard deviation, and the responsiveness 

index. 2,3 Anchor-based methods are based 

on the association between the PRO measure 

and an interpretable external measure, such as a 

global rating of change or a response to treatment. 

These methods may result in somewhat 

From Health Outcomes Solutions, Winter Park, Florida; Biomedical 

Statistics and Methodology, Neuropsychiatric Research 

Institute, Fargo, North Dakota; Global Biostatistics, 

Global Development, and Global Health Economics, Amgen, 

Inc., Thousand Oaks, California. 

Manuscript received June 25, 2010; Accepted December 3, 

2010. 

Correspondence to: Susan D. Mathias, Health Outcomes 

Solutions, PO Box 2343, Winter Park, FL 32790; telephone: 

(407) 643-9016; fax: (866) 384-0194; e-mail: smathias@ 

healthoutcomessolutions.com 

J Support Oncol 2011;9:72–78 © 2011 Elsevier Inc. All rights reserved. 

doi:10.1016/j.suponc.2010.12.004 

Abstract The Brief Pain Inventory–Short Form (BPI-SF) is widely used for 

assessing pain in clinical and research studies. The worst pain rating is often 

the primary outcome of interest; yet, no published data are available on its 

minimally important difference (MID). Breast cancer patients with bone 

metastases enrolled in a randomized, double-blind, phase III study comparing 

denosumab with zoledronic acid for preventing skeletal related 

events and completed the BPI-SF, FACT-B, and EQ-5D at baseline, week 5, 

and monthly through the end of the study. Anchor- and distribution-based 

MID estimates were computed. Data from 1,564 patients were available. 

Spearman correlation coefficients for anchors ranged from 0.33–0.65. 

Mean change scores for worst pain ratings corresponding to one-category 

improvement in each anchor were 0.26–1.04 for BPI-SF current pain, �1.40 

to �2.42 for EQ-5D Index score, 1.71–1.98 for EQ-5D Pain item, �2.22 to 

�0.51 for FACT-B TOI, �1.61 to �0.16 for FACT-G Physical, and �1.31 to 

�0.12 for FACT-G total. Distribution-based results were 1 SEM � 1.6, 0.5 

effect size � 1.4, and Guyatt’s statistic � 1.4. Combining anchor- and 

distribution-based results yielded a two-point MID estimate. An MID estimate 

of two points is useful for interpreting how much change in worst 

pain is considered clinically meaningful. 

different estimates, and no particular estimate is 

considered the most valid. 2–4 Therefore, researchers 

are encouraged to use more than one 

method and to present a range of MID estimates. 

A frequently used PRO measure for the assessment 

of pain is the Brief Pain Inventory–Short 

Form (BPI-SF). The foundation of the BPI-SF is 

the Wisconsin Brief Pain Questionnaire, which 

was developed over 25 years ago based on interviews 

with cancer patients, expert opinion, and 

then-current psychometric standards. 5 Over 

time, the Wisconsin Brief Pain Questionnaire 

evolved into the Brief Pain Inventory, which was 

later reduced to a shorter version, the BPI-SF. 

Today, the BPI-SF is the standard for clinical and 

research use. It has been used in over 400 studies, 

including psychometric evaluations and clinical 

applications with a wide range of conditions 


(e.g., cancer pain, fibromyalgia, neuropathic pain, and 

joint diseases). 6 

The BPI-SF includes two domains: pain severity and pain 

interference. The pain severity domain, the focus of this 

report, includes items specific to pain at “worst,” “least,” 

“average,” and “now” (current pain), with a numerical response 

scale ranging from 0 (no pain) to 10 (pain as bad as 

you can imagine). In clinical trials, the worst pain item has 

been used alone as a measure of pain severity. 6 Its use as a 

single item is supported by a consensus panel on outcome 

measures for chronic pain clinical trials. 7 In addition, the 

Food and Drug Administration’s (FDA) guidance on PROs 

states that a single-item PRO measure of pain severity is 

appropriate for assessing the effect of a treatment on pain. 8 

Although extensive psychometric evaluation of the BPI-SF 

has been conducted, no estimates of the MID are available 

for the BPI-SF worst pain item. Establishing the MID for 

the BPI-SF worst pain item is important because it will 

provide a clinically relevant reference to interpret changes 

in pain scores. Therefore, the objective of this current 

report was to estimate the MID of the worst pain item of 

the BPI-SF. 

Methods 

STUDY DESIGN 

Patients with advanced breast cancer and bone metastases 

were enrolled in an international, randomized, double-blind, 

double-dummy, active-controlled phase III study comparing 

denosumab with zoledronic acid for delaying or preventing 

skeletal related events. Patients were eligible to participate if 

they had histologically or cytologically confirmed breast adenocarcinoma; 

current or prior radiologic, computed tomography, 

or magnetic resonance imaging evidence of at least one 

bone metastasis; and an Eastern Cooperative Oncology Group 

(ECOG) performance status of 0, 1, or 2. Patients with current 

or prior intravenous bisphosphonate administration were 

excluded. Patients completed PRO assessments, including the 

BPI-SF, at baseline, week 5, and every 4 weeks thereafter until 

the end of the study. Assessments were scheduled to take 

place prior to any study procedures and prior to study drug 

administration. Although data collection continued, PRO 

analyses for efficacy were truncated when approximately 30% 

of patients dropped out of the study due to death, disease 

progression, or withdrawn consent. 

OUTCOME MEASURES AND ASSESSMENT INTERVALS 

A number of outcome measures were assessed in the study 

and considered for use as anchors for evaluating the MID of 

the BPI-SF worst pain item, including one clinician-reported 

measure (ECOG Performance Status) and several PRO measures: 

the EuroQoL 5 Dimensions (EQ-5D) Index score, the 

Functional Assessment of Cancer Therapy-Breast Cancer 

(FACT-B), and the BPI-SF current pain rating. 

The ECOG Performance Status, which assesses how a 

patient’s disease or its treatment is progressing and how the 

disease affects the daily living abilities of the patient, is a 

Mathias et al 

single-item, six-point, clinician-rated assessment of performance 

ranging from 0 (fully active, no restrictions) to 5 

(dead). 9 The EQ-5D Index score is a measure of health status, 

which assesses five dimensions: mobility, self-care, usual activities, 

pain/discomfort, and anxiety/depression. Each dimension 

is comprised of three response options: no problems, 

some/moderate problems, and extreme problems. Responses 

are converted to a weighted health state index, with scores 

ranging from �0.594 (worst health) to 1.0 (full health). The 

single item on pain from the EQ-5D was also evaluated 

separately as an anchor. The FACT-B includes the four core 

FACT-General (FACT-G) dimensions of physical well-being, 

social/family well-being, emotional well-being, and functional 

well-being, for which scale scores and a total score can 

be computed. In addition, the FACT-B includes a breast 

cancer–specific subscale. 10 The FACT-B Trial Outcome Index 

(TOI) is the sum of the physical well-being score, the 

functional well-being score, and the breast cancer subscale. 

The four FACT-G scale scores, the FACT-G total score, the 

FACT-B TOI, and a single-item overall quality-of-life (QOL) 

rating from the functional well-being section were all evaluated 

as potential anchors. The single-item overall QOL item 

from the functional well-being scale was selected to balance 

out the single item on pain that was selected from the EQ-5D, 

by serving as a more general potential anchor in breadth and 

scope. For all of these FACT outcome measures, a higher 

score indicates better health-related QOL. Finally, the current 

pain rating from the BPI-SF, ranging from 0 (no pain) to 

10 (pain as bad as you can imagine), was also considered as an 

anchor because it was hypothesized to be highly correlated 

with the worst pain rating and because it would assist in 

understanding the behavior of other potential anchors. 

Several assessment intervals were considered for evaluation 

of the MID for the BPI-SF worst pain item: baseline to 

week 5, baseline to week 13, and baseline to week 25. The 

analysis for each time interval included only those patients 

with complete baseline and end-of-interval (i.e., week 5, 

week 13, or week 25) assessments on the BPI-SF worst pain 

item and the relevant anchor of interest. In addition, a post 

hoc confirmatory analysis was conducted using a longer interval 

of time, from baseline to week 49. No imputation of 

missing data was performed. Analysis was performed on 

pooled data, regardless of treatment assignment. 

ANCHOR-BASED ANALYSIS 

The usefulness of an anchor depends on the correlation 

of the PRO change score and the anchor. 11 Therefore, to 

select the most appropriate anchors and time interval for estimating 

the MID for the BPI-SF worst pain item, Spearman 

correlation coefficients were calculated between changes in 

the BPI-SF worst pain rating and changes in potential anchors 

across each of the potential time intervals. The time interval 

with the highest correlations and the anchors with statistically 

significant (P � 0.05) a priori specified correlations above 0.30 

were selected for inclusion in the MID analysis. 12 

VOLUME 9, NUMBER 1 � JANUARY/FEBRUARY 2011 www.SupportiveOncology.net 73

Estimating MIDs for the Worst Pain Rating of the BPI-SF 

A one-category change was defined as a one-point change 

for the BPI-SF current pain item, a one-point change for the 

EQ-5D pain item, a three-point change for the FACT-G 

Physical Well-Being scale, 13 a six-point change for the 

FACT-G total and FACT-B TOI scores, 14 and a 0.20 change 

for the EQ-5D Index score. For the selected interval and 

anchors, the mean change in BPI-SF worst pain item that 

corresponds to a one-category increase and decrease in each 

anchor was calculated. In addition, ordinary least squares 

regression models were used to regress changes in BPI-SF 

worst pain ratings on changes of each of the anchors. 15,16 The 

regression models included main effects for change in each 

anchor and an interaction term expressing the change in 

anchor-by-baseline anchor. 

DISTRIBUTION-BASED ANALYSIS 

The following distribution-based measures were calculated 

for the BPI-SF worst pain item: (1) the SEM, (2) effect size 

(Cohen’s d), and (3) Guyatt’s statistic. The SEM is a measure 

of the precision of a test instrument. It is calculated on the 

basis of sample data using the sample standard deviation and 

the sample reliability coefficient. While the standard deviation 

and the reliability of a measure are sample-dependent, 

their relationship (and hence the SEM) remains relatively 

constant across samples. Therefore, the SEM is considered to 

be an attribute of the measure and not a characteristic of the 

sample per se. 17 Threshold values of 1 SEM have been suggested 

for defining clinically meaningful differences. 18 The 

reliability coefficient was estimated for the BPI-SF worst pain 

item by calculating the intraclass correlation coefficients 

(ICCs) using two intervals of time. One used 7 days (days 

1–8), a more typical interval for assessing reproducibility, 

while the other approach used a later interval, from week 105 

to week 109. (Note: The 1-month interval was dictated by 

the schedule of assessments.) For both ICC values, only those 

patients whose FACT-B overall QOL ratings changed by 10% 

or less during the respective intervals were included. The 10% 

criterion was selected after reviewing the full distribution of 

change scores and their associated sample sizes, to arrive at a 

reasonable sample size of approximately 100 subjects. 

Cohen’s d, alternatively referred to as the “standardized 

effect size,” is calculated by dividing the difference between 

the baseline and week-25 scores by the standard deviation at 

baseline. 19 The effect size represents individual change in 

terms of the number of baseline standard deviations. A value 

of 0.20 is a small effect, 0.50 is a medium effect, and 0.80 is 

a large effect. Effect sizes of 0.20, 0.50, and 0.80 were calculated 

in this study. 

Guyatt’s statistic, also referred to as the “responsiveness 

statistic,” is calculated by dividing the difference between 

baseline and week-25 change by the standard deviation of 

change observed for a group of stable patients. 20 The denominator 

of the responsiveness statistics adjusts for spurious 

change due to measurement error. Values of 0.20 and 0.50 

have been used to represent “small” and “medium” changes, 

respectively. 21 Values representing 0.20 and 0.50 were calcu- 

Table 1 

Demographic Characteristics 

CHARACTERISTIC, n (%) STUDY SAMPLE (n � 1,564) 

Gender 

Female 1,550 (99.1) 

Male 14 (0.9) 

Age, mean years � SD (range) 

Race 

57.2 � 11.2 (27.1–91.2) 

White 1,265 (80.9) 

Black 38 (2.4) 

Hispanic 92 (5.9) 

Japanese 119 (7.6) 

Asian 28 (1.8) 

Other 22 (1.4) 

Demographic characteristics including the breakdown by gender, age, and race for the 

study sample are shown. 

lated in this study. Stable patients were defined as those 

whose ECOG Performance rating did not change during the 

assessment interval. A different variable was used in defining 

the stable population for purposes of calculating the SEM and 

Guyatt’s statistic because both variables were not consistently 

collected on the same schedule of assessments. 

INTEGRATING ANCHOR-BASED AND DISTRIBUTION-BASED 

MID ESTIMATES 

The minimal detectable change (MDC) for the worst pain 

item was established by comparing distribution-based estimates. 

The MDC represents the smallest change that can 

be reliably distinguished from random fluctuation and, 

thus, the lower bound for establishing the MID. 11 If the 

MID were lower than the MDC, then the instrument would 

not be capable of distinguishing the MID. The SEM was 

considered the primary distribution-based estimate because it 

takes into account the reliability of the measure and, thus, 

estimates the precision of the instrument. 11 Other distribution-based 

measures were also considered in establishing the 

MDC. Standardized effect size was considered a secondary 

distribution-based estimate because of its reliance on interperson 

variability, which is generally higher and less consistent 

than intraperson variability. Anchor-based estimates of 

the MID range were then compared. A final MID range was 

established that is greater than the MDC and integrates 

estimates from the various anchors. 

Results 

PATIENT POPULATION 

Demographic and clinical characteristics for patients included 

in the baseline to week 25 interval are presented in 

Table 1. Data from 1,564 of 2,049 patients who participated 

in the study and had valid (i.e., nonmissing) baseline and endof-interval 

scores for the BPI-SF and anchors were used in these 

analyses. Patients were predominantly female with an average 

age of 57.2 � 11.2 years. The majority of patients were white 


Table 2 

Spearman Correlation Coefficients between Changes in BPI-SF Worst Pain Rating and Changes in Potential 

Anchors 

ANCHOR 

BASELINE TO WEEK 5 

ASSESSMENT INTERVAL 

BASELINE TO WEEK 13 BASELINE TO WEEK 25 

BPI-SF Current Pain rating 0.234* (n � 1,814) 0.275* (n � 1,730) 0.647* (n� 1,562) 

EQ-5D Index score �0.286* (n � 1,742) �0.331* (n � 1,667) �0.391* (n� 1,507) 

EQ-5D Pain item 0.359* (n � 1,774) 0.383* (n � 1,696) 0.423* (n� 1,543) 

ECOG Performance scale — 0.162* (n � 1,702) 0.164* (n � 1,542) 

FACT-B Overall QOL �0.131* (n � 1,783) �0.155* (n � 1,705) �0.195* (n � 1,543) 

FACT-B TOI �0.252* (n � 1,740) �0.318* (n � 1,665) �0.351* (n� 1,514) 

FACT-G Physical Well-Being �0.339* (n � 1,795) �0.381* (n � 1,715) �0.420* (n� 1,554) 

FACT-G Social/Family Well-Being �0.026 (n � 1,785) �0.018 (n � 1,705) �0.048 (n � 1,550) 

FACT-G Emotional Well-Being �0.151* (n � 1,788) �0.176* (n � 1,713) �0.188* (n � 1,552) 

FACT-G Functional Well-Being �0.138* (n � 1,790) �0.199* (n � 1,710) �0.227* (n � 1,551) 

FACT-G total score �0.255* (n � 1,760) �0.288* (n � 1,687) �0.329* (n� 1,522) 

* P � .001. 

Bolded correlations represent the highest correlations with anchors where correlation r � 0.300. 

Spearman correlation coefficients between changes in BPI-SF worst pain rating and changes in each of the 11 potential anchors that were considered are provided. The data are displayed 

for three intervals of time including baseline to week 5, baseline to week 13, and baseline to week 25. Using a cut point of r � 0.300, only those correlations that are bolded meet the criteria 

of acceptability. 

(80.9%). Average pain scores at baseline were 2.45 � 2.51, with 

a full range of scores (0–10) being used. Clinical results from the 

study have been presented previously. 22 

ANCHOR-BASED ANALYSIS 

Spearman correlations between changes in the BPI-SF 

worst pain item and changes in potential anchors are presented 

in Table 2. For all potential anchors, the highest 

correlations with the BPI-SF worst pain rating were obtained 

at the baseline to week 25 interval. All potential anchors 

correlated significantly (P � 0.001) with the BPI-SF worst 

pain rating with the exception of the FACT-G Social/Family 

Well-Being scale. However, correlations were low (�0.30) for 

several potential anchors: ECOG Performance Status, 

FACT-B Overall QOL item, FACT-G Emotional Well-Being, 

and FACT-G Functional Well-Being. Therefore, the 

week 25 interval and the following anchors were selected for 

the MID analysis: BPI-SF current pain rating, EQ-5D Index 

score, EQ-5D Pain item, FACT-B TOI, FACT-G Physical 

Well-Being, and FACT-G total score. Correlation coefficients 

between the changes in the selected anchors and 

changes in the BPI-SF worst pain ratings range from 

0.329–0.647. 

Mean changes in the BPI-SF worst pain rating that correspond 

to a one-category change in anchors from baseline to 

week 25 are presented in Table 3. BPI-SF current pain ratings 

�5 and EQ-5D Index scores �0.40 were excluded from their 

respective analysis due to small sample sizes. A one-category 

increase in the anchor scores was associated with an absolute 

value of change in the BPI-SF worst pain item ranging from 

0.26–2.42. A one-category decrease in the anchor score was 

associated with an absolute value of change in the BPI-SF 

worst pain item ranging from 0.56–3.16. Changes associated 

Mathias et al 

Table 3 

Range of Mean Changes in BPI-SF Worst Pain Rating 

from Baseline to Week 25 by Anchors 

ONE CATEGORY 

ANCHOR 

A ONE CATEGORY 

INCREASE IN DECREASE IN 

ANCHOR 

ANCHOR 

BPI-SF Current Pain rating 0.26–1.04 �0.89 to �1.66 

EQ 5D Index score �2.42 to �1.40 0.56–1.63 

EQ 5D Pain item 1.71–1.98 �3.16 to �2.56 

FACT-B TOI �2.22 to �0.51 �0.56 to 0.77 

FACT-G Physical Well-Being �1.61 to �0.16 �0.79 to 0.46 

FACT-G total �1.31 to �0.12 �0.97 to 0.57 

a One category (increase or decrease) represents 0.20 points for EQ-5D Index score, one 

point for BPI-SF current pain rating and EQ-5D pain item, three points for FACT-G Physical 

Well-Being, and six points for FACT-G total and FACT-B TOI. 

The range of mean changes in BPI-SF worst pain ratings (using the interval from baseline 

to week 25) for the six anchors that met the correlation criteria in Table 2 are provided. 

Mean changes are displayed for one-category increases and one-category decreases in 

anchor. 

with improvement and worsening in anchors were not symmetrical, 

nor was there a consistent trend across anchors. For 

example, for the EQ-5D pain item, the magnitude of 

change in BPI-SF worst pain was greater for a one-category 

increase in the anchor than for a one-category decrease in 

the anchor. In contrast, for the EQ-5D Index score, the 

magnitude of change in BPI-SF worst pain was greater for 

a one-category decrease in the anchor than for a onecategory 

increase in the anchor. 

The regression of changes in anchors on changes in the 

BPI-SF worst pain item is shown in Table 4. Changes in each 

anchor are significantly (P � 0.05) associated with changes in 

BPI-SF worst pain rating. A one-point increase in BPI-SF 



Table 4 

Regression of Changes in BPI Worst Pain Item on Changes in Anchors and Baseline Anchor Ratings 

VARIABLE PREDICTOR b � SIG. 

Change in BPI current pain Main effect 0.817 0.724 �0.001 

Interaction with baseline anchor �0.024 �0.107 0.001 

Change in EQ-5D Health State Index Main effect �3.548 �0.349 �0.001 

Interaction with baseline anchor 0.220 0.021 0.465 

Change in EQ-5D Pain item Main effect 1.805 0.352 �0.001 


Change in FACT-B TOI Main effect �0.098 �0.406 �0.001 


Change in FACT-G Physical Well-Being Main effect �0.163 �0.321 �0.001 

Interaction with baseline anchor �0.004 �0.133 0.024 

Change in FACT-G total score Main effect �0.048 �0.231 0.025 

Interaction with baseline anchor 0.000 �0.130 0.209 

b, regression coefficient; �, standardized regression coefficient; Sig., significance level. 

Possible ranges: BPI Pain Right Now 0 (least) to 10 (most), EQ-5D Health State Index scores �0.594 (worst) to 1.00 (best), EQ-5D Pain item scores 1 (none) to 3 (severe), FACT-B TOI scores 

4 (worst) to 92 (best), FACT-G Physical Well-Being scores 0 (worst) to 28 (best), FACT-G total score 8 (worst) to 108 (best), BPI Worst Pain item 0 (least) to 10 (most). 

Changes in all anchors are significantly (P � 0.05) associated with changes in BPI-SF worst pain ratings. A one-point increase in BPI-SF current pain rating and EQ-5D pain item is associated 

with increases (positive b score) in the BPI-SF worst pain rating, and a one-point increase in EQ-5D Index, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total scores is associated with 

decreases (negative b score) in the BPI-SF worst pain ratings. The change in anchor-by-baseline anchor interaction was statistically significant only for the BPI current pain and FACT-G PWB 

items. 

Table 5 

Distribution-Based Measures of the BPI-SF Worst Pain Rating 

STANDARD ERROR OF MEASUREMENTA EFFECT SIZEB GUYATT’S STATISTICC r (Day 1–8) r (Week 105–109) 0.20 0.50 0.80 0.20 0.50 

1.599 1.274 0.570 1.425 2.279 0.567 1.417 

a 

The standard error of measurement is a measure of the precision of a test instrument. It is calculated on the basis of sample data using the sample standard deviation and the sample 

reliability coefficient. Intraclass correlation coefficients (ICCs) for BPI-SF worst pain rating from day 1 to day 8 and week 105 to week 109 in patients whose FACT-B overall QOL ratings change 

by �10% are 0.685 (n � 926) and 0.800 (n � 109), respectively. 

b 

Alternatively referred to as Cohen’s d, the effect size is calculated by dividing the difference between the pretest and posttest scores by the standard deviation at pretest. The standard 

deviation of BPI-SF worst pain rating at baseline (n � 1,877) is 2.849. 

c 

Alternatively referred to as the responsiveness statistic, Guyatt’s statistic is calculated by dividing the difference between pretest and posttest changes by the standard deviation of change 

observed for a group of stable patients. The standard deviation of change in BPI-SF worst pain rating from baseline to week 25 in patients whose ECOG performance rating does not change 

(n � 1,120) is 2.833. 

The results from the three distribution-based approaches presented in this table will be combined with those of the anchor-based results to estimate the MID. 

current pain rating and EQ-5D Pain item is associated with a 

0.817 and 1.805 increase in BPI-SF worst pain, respectively, 

while a one-point increase in EQ-5D Index score, FACT-B 

TOI, FACT-G Physical Well-Being, and FACT-G total is 

associated with a 3.548, 0.098, 0.163, and 0.048 decrease in 

BPI-SF worst pain rating, respectively. Likewise, a twopoint 

increase in BPI-SF current pain rating and EQ-5D 

Pain item is associated with a 1.634 and 3.610 increase in 

BPI-SF worst pain, respectively, while a two-point increase 

in EQ-5D Index score, FACT-B TOI, FACT-G Physical 

Well-Being, and FACT-G total is associated with a 7.096, 

0.196, 0.326, and 0.096 decrease in BPI-SF worst pain 

rating, respectively. The change in anchor-by-baseline anchor 

interaction was statistically significant only for BPI 

current pain and FACT-G Physical Well-Being. The interaction 

tests whether the anchor–BPI-SF slope differs as 

a function of baseline anchor score; therefore, a lack of 

significance suggests that the association between BPI-SF 

worst pain and other anchors does not differ by baseline 

anchor rating. 

A post hoc confirmatory analysis was done replicating 

these analyses using data from the baseline to week 49 interval 

(n � 1,250). Results indicate a slightly stronger correlation 

between the anchors and the change scores. (Spearman’s 

correlations range from 0.372 for FACT-TOI to 0.644 for 

BPI-SF current pain rating.) Mean change scores of BPI-SF 

worst pain ratings by each of the six anchors and regression 

coefficients were similar to those for the baseline to week 25 

interval. For instance, mean change scores for the EQ-5D 

Pain item for stable patients ranged from 0.25–0.56, 1.58– 

295 for an improvement of one category, and 1.75–2.80 for a 

worsening of one category compared with 0.50–0.51, 1.71– 

1.98, and 2.56–3.16, respectively, for the baseline to week 25 

interval. 


DISTRIBUTION-BASED ANALYSIS 

The distribution-based estimates for the BPI-SF worst pain 

rating are presented in Table 5. There appears to be consistency 

with the 1 SEM estimates, the 0.50 effect size, and the 

0.50 Guyatt’s statistic. 

INTEGRATING ANCHOR-BASED AND DISTRIBUTION-BASED 

MID ESTIMATES 

The distribution-based analyses suggest that the MDC for 

the worst pain rating, defined as the smallest change that can 

be reliably differentiated from random fluctuation, is between 

1.3 and 1.6 points (see Table 5). This represents the lower 

bound for establishing the MID. 

The results from regression analyses can be used to translate 

changes between anchors and corresponding changes in 

BPI-SF worst pain. This strategy can be particularly informative 

when the MID for an anchor is known. This is the case 

for the EQ-5D Health State Index, where the MID has been 

estimated at 0.06 for U.S. Index scores and 0.07 for U.K. 

Index scores. 23 A one-point change in EQ-5D Index translates 

to a change of �3.548 in BPI-SF worst pain, so a 

0.07-point change in EQ-5D Index (the MID for the measure) 

corresponds to a change of �0.248 in BPI-SF worst 

pain. In contrast, a one-point change in BPI-SF worst pain 

(which is smaller than the MID based upon the distributionbased 

analyses) translates to a change of 0.036 for the EQ-5D 

Index score (considerably smaller than the MID of 0.07). 

However, a two-point change in BPI-SF worst pain rating 

corresponds to a 0.072 change in EQ-5D Index score, which 

is almost identical to the MID for that measure. This suggests 

that a two-point change may be a reasonable estimate for the 

MID of the BPI-SF worst pain rating. 

Discussion 

Data from both distribution-based and anchor-based approaches 

were used to develop estimates of the MID for the 

BPI-SF worst pain rating. Results from these approaches 

are similar, providing reasonably strong support for establishing 

a two-point MID for the BPI-SF worst pain rating. 

Further, the results suggest that this estimate of MID is, for 

the most part, independent of baseline BPI-SF worst pain 

ratings. However, there is some evidence to suggest that 

the direction of change (improvement or worsening) may 

be important to consider. A number of reports have suggested 

that a smaller change may be required to be considered 

clinically important when a patient is improving 

compared with worsening. 13 Also, when considered as a 

percentage, a one-point change in any scale has a different 

value for an increase versus a decrease; eg, a change from 2 

to 3 is an increase of 50%, while a change from 3 to 2 is a 

decrease of 33%. Nonetheless, these findings provide important 

information to researchers for interpreting changes 

in the BPI-SF worst pain ratings. 

In addition, although not specific to the BPI worst pain 

rating, the findings of this study are consistent with other 

Mathias et al 

published MID analyses for a similar item. A recent review of 

three studies concluded that, for a numerical rating scale of 

pain intensity ranging 0–10 similar in content to the BPI-SF 

worst pain rating, changes of around two points represent 

“meaningful,” “much better,” or “much improved” reductions 

in chronic pain. 24 

Several factors contribute to the overall strength of the 

current results. First, as frequently recommended in the 

literature, 11 both anchor-based and distribution-based 

methods were used to estimate the MID for the worst pain 

rating. Second, analyses were based on a large sample, 

totaling over 1,500 patients for the baseline to week 25 

assessment interval. A larger sample size will generally 

provide a broader distribution of responses, which will 

likely increase the generalizability of the results. Third, 

multiple anchors were used to evaluate changes in BPI-SF 

worst pain ratings. Fourth, analyses were performed across 

several assessment intervals to determine the strongest 

relationship between BPI-SF ratings and other anchors. 

Finally, the regression analyses provide important information 

about whether baseline differences influence the relationship 

between BPI-SF and other PRO measures. 

Nevertheless, these analyses are not without certain limitations. 

The sample for the current analyses consisted entirely 

of breast cancer patients. It is unclear to what extent these 

results will be relevant for other patient populations. Further 

research is needed to determine whether the MID for the 

BPI-SF worst pain rating established in this sample has 

broader applicability. Also, it must be noted that the recall 

period varied across assessments. The BPI-SF focuses on the 

past 24 hours, the FACT uses the past week, and the EQ-5D 

uses the present moment. It is unclear to what extent these 

differences in recall periods may have influenced the current 

results. Finally, the baseline to week 25 interval was used to 

determine the MID for the BPI-SF worst pain rating based on 

the higher correlations for this interval. Data from baseline to 

week 49 are consistent with these results, providing some confirmatory 

evidence to suggest that these MID estimates are 

stable. 

In conclusion, the findings of the present analyses suggest 

that the MID estimate for the BPI-SF worst pain rating is two 

points. This value provides guidance to researchers using the 

BPI-SF worst pain rating on how to interpret baseline differences 

as well as change scores in the BPI-SF worst pain rating. 

Additional analyses could be done in other populations to 

confirm these findings. 

Acknowledgments: The authors acknowledge the contributions 

of Betsy Tschosik, Health Outcomes Solutions, for 

assistance in the preparation of this manuscript and Vidya 

Setty of Amgen, Inc., for editing assistance. 

Conflicts of interest: This research was funded by Amgen Inc., Thousand 

Oaks, CA in part through a contract to Health Outcomes Solutions. SDM 

and RDC were paid by Health Outcomes Solutions, and YQ, QJ, RD and KC 

are employees of Amgen and own Amgen stock and stock options. 




1. Wyrwich KW, Bullinger M, Aaronson N, et 

al. Estimating clinically significant differences in 

quality of life outcomes. Qual Life Res 2005;14: 

285–295. 

2. Mathias SD, Gao SK, Rutstein M, Snyder 

CF, Wu AW, Cella D. Evaluating clinically 

meaningful change on the ITP-PAQ: preliminary 

estimates of minimal important differences. 

Curr Med Res Opin 2009;25(2):375– 

383. 

3. Yost KJ, Sorensen MV, Hahn EA, Glendenning 

GA, Gnanasakthy A, Cella D. Using multiple 

anchor- and distribution-based estimates to 

evaluate clinically meaningful change on the 

Functional Assessment of Cancer Therapy-Biologic 

Response Modifiers (FACT-BRM) instrument. 

Value Health 2005;8(2):117–127. 

4. Hays RD, Woolley JM. The concept of clinically 

meaningful difference in health-related 

quality-of-life research. How meaningful is it? 

Pharmacoeconomics 2000;18(5):419–423. 

5. Daut RL, Cleeland CS, Flanery RC. Development 

of the Wisconsin Brief Pain Questionnaire 

to assess pain in cancer and other diseases. Pain 

1983;17(2):197–210. 

6. Cleeland C. Brief Pain Inventory User 

Guide. Houston: University of Texas M. D. Anderson 

Cancer Center; 2009. 

7. Dworkin RH, Turk DC, Farrar JT, et al. Core 

outcome measures for chronic pain clinical trials: 

IMMPACT recommendations. Pain 2005;113 

(1–2):9–19. 

8. U.S. Department of Health and Human Services 

Food and Drug Administration (FDA). 

Guidance for Industry: Patient-Reported Outcome 

Measures: Use in Medical Product Devel- 

opment to Support Labeling Claims. Silver 

Spring, MD: FDA; 2009. 

9. Oken MM, Creech RH, Tormey DC, et al. 

Toxicity and response criteria of the Eastern Cooperative 

Oncology Group. Am J Clin Oncol 

1982;5(6):649–655. 

10. Brady MJ, Cella DF, Mo F, Bonomi AE, et 

al. Reliability and validity of the Functional Assessment 

of Cancer Therapy–Breast Cancer 

Quality of Life instrument. J Clin Oncol 1997;15: 

974–986. 

11. Crosby RD, Kolotkin RL, Williams GR. Defining 

clinically meaningful change in healthrelated 

quality of life. J Clin Epidemiol 2003; 

56(5):395–407. 

12. Revicki D, Hays RD, Cella D, Sloan J. Recommended 

methods for determining responsiveness 

and minimally important differences 

for patient-reported outcomes. J Clin Epidemiol 

2008;61(2):102–109. 

13. Cella D, Hahn EA, Dineen K. Meaningful 

change in cancer-specific quality of life scores: 

differences between improvement and worsening. 

Qual Life Res 2002;11(3):207–221. 

14. Eton DT, Cella D, Yost KJ, et al. A combination 

of distribution- and anchor-based approaches 

determined the minimally important 

differences (MIDs) for four endpoints in a 

breast cancer scale. J Clin Epidemiol 2004;57: 

898–910. 

15. Weibe S, Matijevic S, Eliasziw M, Derry PA. 

Clinically important change in quality of life in 

epilepsy. J Neurol Neurosurg Psychiatry 2002;73: 

116–120. 

16. Miller KL, Walt JG, Mink DR, et al. Minimal 

clinically important difference for the ocular sur- 

face disease index. Arch Ophthalmol 2010;128(1): 

94–101. 

17. Wyrwich KW, Tierney WM, Wolinsky FD. 

Further evidence supporting an SEM-based criterion 

for identifying meaningful intra-individual 

changes in health-related quality of life. J Clin 

Epidemiol 1999;52(9):861–873. 

18. Wolinsky FD, Wan GJ, Tierney WM. 

Changes in the SF-36 in 12 months in a clinical 

sample of disadvantaged older adults. Med Care 

1998;36(11):1589–1598. 

19. Cohen J. Statistical Power Analysis for the 

Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence 

Erlbaum; 1988. 

20. Guyatt GH, Bombardier C, Tugwell PX. 

Measuring disease-specific quality of life in clinical 

trials. CMAJ 1986;134(8):889–895. 

21. Norman GR, Stratford P, Regehr G. Methodological 

problems in the retrospective computation 

of responsiveness to change: the lesson 

of Cronbach. J Clin Epidemiol 1997;50(8): 

869–879. 

22. Stopeck A, Body J, Fujiwara Y, et al. Denosumab 

versus zoledronic acid for the treatment 

of breast cancer patients with bone metastases: 

results of a randomized phase 3 study. 

Eur J Cancer Suppl 2009;7:2. 

23. Pickard AS, Neary MP, Cella D. Estimation 

of minimally important differences in EQ-5D utility 

and VAS scores in cancer. Health Qual Life 

Outcomes 2007;5:70. 

24. Dworkin RH, Turk DC, Wyrwich KW, et al. 

Interpreting the clinical importance of treatment 

outcomes in chronic pain clinical trials: IMMPACT 

recommendations. J Pain 2008;9(2):105–121. 



A Pilot Trial of Decision Aids to Give 

Truthful Prognostic and Treatment 

Information to Chemotherapy Patients 

with Advanced Cancer 

Thomas J. Smith, MD, Lindsay A. Dow, MD, Enid A. Virago, MDiv, James Khatcheressian, MD, 

Robin Matsuyama, PhD, and Laurel J. Lyckholm, MD 

Patients with incurable disease state that 

they want truthful information about their 

diagnosis, treatment options, and course 

even if the outlook is poor; 1 but most patients 

never receive information from their physicians 

about prognosis 2 or even imminent death. 3 U.S. 

physicians do not disclose prognosis at least half 

the time and feel unprepared to have these discussions. 

4 Not having a discussion about imminent 

death is associated with worse quality of 

care, worse quality of life, worse caregiver quality 

of life, 5 and over $1,000 more in medical care 

cost in the last week of life. 6 Physicians are reluctant 

to give people poor prognostic information 

7 for fear of dashing hope, 8 and Web sites 

such as www.cancer.gov do not contain detailed 

information about prognosis, survival, palliative 

care options, or hospice referrals. 

We designed decision aids for patients with 

incurable cancer and attempted to determine if 

people would opt for full disclosure about prognosis 

and treatment. If they opted for full disclosure, 

we assessed current knowledge about 

chance of cure, survival, disease response rates, 

and symptom control, before and after. This pilot 

trial was done to see if patients would complete a 

From the Massey Cancer Center of Virginia Commonwealth 

University, School of Education, VCU School of Medicine, 

Department of Social and Behavioral Health, and the Virginia 

Cancer Institute, Richmond, Virginia. 

Manuscript submitted September 13, 2010; accepted November 

29, 2010 

Correspondence to: Thomas J. Smith, MD, Virginia Commonwealth 

University, Division of Hematology/Oncology 

and Palliative Care, MCV Box 980230, Richmond, VA 

23298–0230; telephone: (804) 828–9723; fax: (804) 828– 

8079; e-mail: tsmith5@mcvh-vcu.edu 


doi:10.1016/j.suponc.2010.12.005 

Abstract Most cancer patients do not have an explicit discussion 

about prognosis and treatment despite documented adverse outcomes. 

Few decision aids have been developed to assist the difficult 

discussions of palliative management. We developed decision aids for 

people with advanced incurable breast, colorectal, lung, and hormonerefractory 

prostate cancers facing first-, second-, third-, and fourth-line 

chemotherapy. We recruited patients from our urban oncology clinic 

after gaining the permission of their treating oncologist. We measured 

knowledge of curability and treatment benefit before and after the 

intervention. Twenty-six of 27 (96%) patients completed the aids, with 

a mean age of 63, 56% female, 56% married, 56% African American, and 

67% with a high school education or more. Most patients (14/27, 52%) 

thought a person with their advanced cancer could be cured, which 

was reduced (to 8/26, 31%, P � 0.15) after the decision aid. Nearly all 

overestimated the effect of palliative chemotherapy. No distress was 

noted, and hope did not change. The majority (20/27, 74%) found the 

information helpful to them, and almost all (25/27, 93%) wanted to 

share the information with their family and physicians. It is possible to 

give incurable patients their prognosis, treatment options, and options 

for improving end-of-life care without causing distress or lack of hope. 

Almost all find the information helpful and want to share it with doctors 

and family. Research is needed to test the findings in a larger sample 

and measure the outcomes of truthful information on quality of life, 

quality of care, and costs. 

decision aid about their advanced cancer, even if 

it contained truthful information about their 

limited prognosis and treatment benefits. 

Methods 

We created state-of-the-art tables of information 

for patients with advanced breast, lung, colon, 

and hormone-refractory prostate cancers, 

based on expert review, external review, and 

comparison with Up To Date © (available from 

the authors). The information was approved by 


Truthful Information 

all three oncologists involved. We used bar graphs to illustrate 

benefit, developed for patient education graphs for a randomized 

study of insurance types and treatment choices 9 and in 

common use on the Web site Adjuvant Online (www. 

adjuvantonline.org). 10,11 It is similar to what we do with the 

written medical record, a concise review of diagnosis, prognosis, 

treatment options, side effects, and when to call the 

doctor. 12 

We tested the intervention in a heterogeneous sample of 

27 patients recruited through the Dalton Oncology Clinic, 

which serves a mix of patients from the most discerning 

third-opinion clinical trial patient to the community cancer 

patient and provides most of the indigent cancer care in the 

central Virginia area. The study was done within 3 months in 

early 2009. 

Our primary outcome was the number of patients who 

would opt for full disclosure once they viewed the decision 

aid. Our secondary outcomes included the following: the 

amount of information patients have about cure, response 

rates, and symptom control; the impact of truthful information 

on hope, as measured by the Herth Hope Index ©13 

(HHI) used to assess hope in clinical studies of adults; 14 

whether the information was deemed helpful to the patient; 

and whether the patient intended to share the information 

with a doctor. 

Patients were accrued by reviewing the daily clinic list to 

find patients on treatment for incurable breast, colorectal, 

non-small-cell lung, or hormone-refractory prostate cancer. 

Treating oncologists were made aware of the study through 

e-mail, announcements, the Massey Cancer Center Web site, 

and individual meetings. All oncologists approached agreed 

to their chemotherapy patients participating in the study in 

general, and the primary nurse or treating oncologist was 

contacted about each eligible patient. Eligible patients were 

not contacted about the study when the treating oncologist or 

primary oncology nurse determined that a patient was experiencing 

significant distress or had significant psychiatric 

problems or difficulty with adjustment to illness or believed 

the patient would have great emotional difficulty with the 

information. The number of patients excluded by each oncologist 

due to concern about distress was estimated to be less 

than 10% of the total available but was not measured. Since 

these patients were not enrolled in the study, we did not 

collect information about them. A clinical psychologist and a 

chaplain were available to any patient who experienced distress 

during or after the interview process. The interview 

questions and intervention were administered by a member of 

the study team who was not the patient’s oncologist or involved 

in his or her care. The interview team included a 

graduate student who was also a minister and chaplain 

(E. A. V.), a medical student with special training in empathic 

communication (L. A. D.), and/or the principal investigator 

(T. J. S.); usually one interviewer was present (L. A. D. 

or E. A. V.). 

The interview sequence included screening questions to 

ensure that the patient wanted full information, sociodemo- 

graphic questions, a pretest about the chance of cure and 

treatment effect for a patient with their illness, and the HHI. 

Next, the decision aid was administered. Immediately afterward, 

the patient completed a posttest, the HHI, and information 

about how he or she would use the information. 

We modeled this approach on the Ottawa Decision Support 

Framework, a clinically tested decision-making tool designed 

to inform decisional conflict, 15,16 defined as uncertainty 

about which course of action to take when the choice involves 

balancing gain, risk, loss, regrets, or challenges to 

personal life. 17 

Our study was approved by the Massey Cancer Center 

Protocol Review and Monitoring System and the VCU Institutional 

Review Board for the Conduct of Human Research. 

Because it was not a clinical trial, no clinical trial 

registration was required. 

Results 

The patients were typical for our urban, tertiary referral, 

and safety net hospital and National Cancer Institute–designated 

cancer center, as shown in Table 1. 

PRIMARY OUTCOME 

Our primary outcome was to assess if patients would complete 

a decision aid with full disclosure. Of 27 patients, only 

one (4%) chose not to complete the decision aid after starting. 

She was a 55-year-old African American woman who had 

recently started first-line treatment for metastatic colorectal 

cancer. She had been told at another institution that she had 

lost too much weight and was too ill to benefit from chemotherapy, 

but with counseling she regained the weight and had 

a performance status of 2 at VCU. In her pretest, she answered 

that she thought a woman with metastatic colorectal 

cancer spread to bones and lymph glands could be cured, with 

a chance of cure of 50%. Once presented with the information 

(good treatments that prolong life and control symptoms 

but no chance of cure and 9% of patients with metastatic 

colorectal cancer alive at 5 years), she said that she did not 

want to finish the questions. She did complete her HHI, 

which did not change, and was not distressed (see Table 2, 

patient 12). 

In the pretest, almost all the patients, including the patient 

above, reported wanting full disclosure about cancer, prognosis, 

treatment, and side effects. In response to questions beginning 

“How much do you want to know about . . .” 27 of 27 

answered “Tell me all” to the questions about “your cancer,” 

“your prognosis,” “treatment benefits,” and “treatment side 

effects.” Only one of 27 answered otherwise: “Tell me a little” 

about cancer, and “Tell me some” about prognosis. 

SECONDARY OUTCOMES 

Participants were overoptimistic about the results of palliative 

chemotherapy, as shown in Table 3. Most (14/27, 52%) 

people thought a person with “metastatic cancer (breast, 

colorectal, lung, prostate—specific to that person’s disease) 

spread to the bones and lymph glands” could be cured. After 

the decision aid, more people recognized that their cancer 


Table 1 

Patient Demographic and Disease Characteristics, 

n � 26 

Age (years) 

Mean 63 � 5 

Range 

Gender 

46–74 

Male 12 (44%) 

Female 

Marital status 

15 (56%) 

Married or committed relationship 15 (56%) 

Divorced 6 (22%) 

Widowed 2 (7%) 

Single/never married 

Ethnicity 

2 (7%) 

Caucasian 12 (44%) 

African American 

Education completed 

15 (56%) 

Less than high school 5 (9%) 

Some high school 1 (4%) 

HS diploma/GED 8 (30%) 

Some college 10 (37%) 

Completed college 1 (4%) 

Completed postgrad 

Total household income 

2 (7%) 

�$15,000 10 (37%) 

$15,000–$34,999 8 (30%) 

$35,000–$74,999 6 (22%) 

�$75,000 1 (4%) 

Don’t know 2 (7%) 

Average number of people in 

household 

Type of cancer and line of 

chemotherapy 

2.3 (SD 0.9) 

Breast 1st, 2nd, 3rd, 4th line 5, 2, 1,1 � 9 total 

Colorectal 1st, 2nd, 3rd, 4th line 8, 5, 1, 0 � 14 total 

Lung 1st, 2nd, 3rd, 4th line 2, 0, 0, 0 � 2 total 

Hormone-refractory prostate 1st, 

2nd, 3rd, 4th line 

1, 1, 0, 0 � 2 

could not be cured (17/25, 63%) but eight of 25 (32%, P � 

0.15, Fischer’s exact test) still thought a person with metastatic 

disease could be cured. Patients were particularly overoptimistic 

about the chance of their symptoms being helped 

by chemotherapy: 87% thought their symptoms would be 

helped by chemotherapy, and 60% thought a patient would 

have at least 50% shrinkage of their cancer before the exercise, 

which declined only slightly after the decision aid. 

(While the correct answer varies by disease, the number 

helped by chemotherapy is usually less than 50%, and response 

rates are always less than 50%.) 

There was no change in responses to the HHI after the 

intervention as we have previously reported. 18 Participants 

did not appear to be visibly distressed by the intervention. A 

psychologist and chaplain were made available, but no one 

Smith, Dow, Virago, et al 

requested their services. In our small clinic, the primary 

nurses and doctors have frequent interactions during visits 

and chemotherapy. No patient was reported to be distressed 

in any way, during that visit or subsequent visits. 

The comments recorded by the patients or the interviewers 

at the end of the exercise showed that most patients would 

share the information, as shown in Table 4. 

In some cases, the average prognosis and treatment benefit, 

although small, was bigger than the person thought before the 

exercise. Nearly all found it helpful. Some illustrative comments 

are shown in Table 2. 

We did not formally measure the time to complete the 

screening questions, pre- and posttests, pre- and post-HHI, 

and decision aid; but in most cases it took less than 20 

minutes to complete the whole package including the preand 

post-tests. Review of the decision aid with the patient 

always took less than 5 minutes, even when we were reading 

it with the patient and family. This is consistent with work 

showing that oncologists state that completing an advance 

directive will take too much time but, in fact, it takes less 

than 10 minutes. 19,20 

Discussion 

Historical data show that patients know little about their 

prognosis and the effect that treatment will have on their 

cancer. Yet, this knowledge is essential to making informed 

choices about treatment benefits, risks, and even costs. When 

tested in randomized controlled trials, decision aids led to 

more involvement in decision making. 21,22 However, there 

were no decision aids available about metastatic incurable 

disease, despite some promising early starts 23–28 and only one 

about first-line treatment, 29 so we made a simple one. A 

successful decision aid may allow patients to discuss their 

situations with their physicians and develop management 

strategies that best concur with personal goals and preferences 

and help patients make plans in other areas of life. 

Our findings suggest that most people do want honest 

information, even if the news is bad. We found that 27 of 27 

enrolled patients initially reported wanting to know all the 

available information about their cancer, prognosis, treatment 

benefits, and treatment side effects. Also, 26 of 27 

patients were able to complete the decision aid fully, our main 

outcome measure. While approximately 10% of available patients 

were excluded from accrual by their oncologists or 

oncology nurses due to preexisting distress, fear of distress in 

the patient or family member, uncontrolled symptoms, or 

psychiatric illness, in general there was excellent acceptance 

of the study by patients and oncologists. In this pilot study we 

did not investigate the attitudes of nonparticipants nor were 

we able to collect sociodemographic data to determine nonresponse 

bias, that is, whether certain types of patients are 

more likely to decline participation in the study. 

Participants in the study were overoptimistic about their 

chances of cure, potential treatment response, symptom relief, 

and survival. None of these patients had curable disease, but 

63% thought that a person with metastatic cancer of their 



Table 2 

Comments Made by Patients about the Decision Aids 

PATIENT 

SITUATION (1ST-, 2ND-, 3RD-, 

4TH-LINE CHEMOTHERAPY) COMMENT 

1 CRC, 1st “Feel little bit better.” “Didn’t upset.” 

2 BC, 2nd “It gave me information on my condition.” 

3 BC, 4th “If I’ve got 6 months to live, I want to know so I can party” 

3 LC, 1st “It let me know I have longer than a year, possibly longer than that. Helpful.” 

5 PC, 2nd “Well, Dr. R said it couldn’t be cured . . . I’ve done well for 16 months so far.” 

6 CRC, 2nd “We’ve already discussed everything. All information I think is helpful.” 

7 CRC, 1st “. . . happy that my life expectancy might be better than I thought.” At the end, he said 

“how good it was to talk and not hold things in.” 

8 CRC, 3rd “Verification of what I have been told.” 

9 CRC, 2nd “I know all this, but it was helpful. Especially for people who haven’t heard it.” 

10 CRC, 1st 

11 CRC, 2nd “Helpful . . . just to think about my goals and that kind of thing.” 

12 CRC, 1st “Wouldn’t know [about cure]. I can’t answer those . . . [questions about cure rates, 

response rates after reviewing data]. Tell them to give people hope, not take away 

hope . . . not to ’just go smell the roses.’ ” 

13 CRC, 1st “There were some things I didn’t know—I didn’t know about the 1–2 years—I’m not 

going to accept it though—I’m planning on more.” 

14 BC, 1st “Gave me info based on stats that I didn’t know before.” 

15 BC, 1st “It’s hard to explain. It’s about what I have already known.” 

16 BC, 2nd “Helped me to understand . . . . That chemo is better than not having chemo.” 

17 CRC, 1st 

18 CRC, 2nd “Helpful to know what will happen, given strength, how to feel about things . . . to get 

to talk about things.” 

19 BC, 1st “Helpful. It opened my eyes, made me aware. I would want to know that.” 

20 BC, 1st Helpful. “It gave me a lot to think about. A whole lot of it I didn’t know about.” 

21 CRC, 1st Helpful. “Knowing that I was doing something to help someone else. It made me think 

about what I have to look forward to in life.” 

22 CRC, 2nd “In a way, you’re saying what the possibilities are. I just hope that I keep on trucking.” 

23 BC, 3rd “Always helpful to discuss prognosis.” 

24 LC, 1st “Helpful to know what chances I get, with or without (chemotherapy) treatment.” 

25 PC, 1st “Because the odds are a hell of a lot less than I thought, it’s a bummer.” 

26 BC, 1st “It gave me a chance to see the percentage of things with breast cancer. I have a better 

understanding of the time line.” 

26 CRC, 1st “Made me understand some things.” On change in survival from �3 years to “don’t 

know,” “I hope to live a right good while.” 

BC � breast cancer; CRC � colon or rectal cancer; LC � non-small-cell lung cancer; PC � hormone-refractory prostate cancer 

type could be cured and gave the average chance of cure as 

52%. Inaccurate assessment of cure rates decreased postintervention. 

At the pretest 14/27 (52%) believed a person with 

cancer similar to theirs could be cured, which changed to 8/26 

(31%) at the posttest. This agrees with other studies that 

showed that patients mistook palliative radiation for curative 

radiation about one-third of the time, even when provided 

with accurate information. 1,30,31 

Knowledge of prognosis and planning for the future is 

important as there is evidence of benefit to having the discussion 

about treatment outcomes. Recent data show improved 

quality of care, improved quality of life, and improved 

caregiver quality of life if the physician discusses death with 

the patient and family. 5 Transplant patients with advanced 

directives had more than a twofold survival advantage over 

those without them. 27 Conversely, over- or underestimating 

survival or treatment benefit can lead to bad health outcomes. 

Stem-cell transplant patients who were overoptimistic lived 

no longer than those with realistic views. 32,33,34 Cancer patients 

who overestimated their survival were more likely to 

die a “bad” death (defined as death in an intensive care unit, 

on a ventilator, or with multiple hospitalizations and emergency 

room visits) without achieving life extension. 35 It may 

be that the 16%–20% of patients with incurable solid tumors 

who start a new chemotherapy regimen within 2 weeks of 

death, 36 when they are unlikely to benefit, simply do not 

know the prognosis or treatment effect or have different 

perspectives. 37 Alternatively, we do not know how many 

patients decline second- and nth-line chemotherapy without 

knowing the full benefits and risks and who might choose 


Table 3 

Patient Knowledge about Palliative Chemotherapy before and after the Decision Aid 

Can this person with cancer in the 

bones and lymph nodes be cured 

by medical treatment? 

What is the chance of her _____ 

cancer shrinking by half? In % 

What is the chance of _____ 

cancer symptoms being helped? 

In % 

chemotherapy if they knew second- or nth-line chemotherapy 

improved survival, pain scores, or quality of life. For instance, 

40% of breast cancer patients will have some disease control 

from fourth-line chemotherapy for up to 4 months even if 

there is no evidence of improved survival. 38 

Patients consistently tell us to be truthful, compassionate, 

and clear and to stay the course with them. 39,40 Despite nearly 

all American patients stating that they want full disclosure 

about their prognosis, treatment options, and expected outcomes, 

most patients do not receive such information 41 or 

receive such information far too late in their course. 42 Even if 

terminally ill patients with cancer requested survival estimates, 

doctors would provide such estimates only 37% of the 

PRE POST CHANGE COMMENT 

Yes � 14 

No � 11 

Don’t know � 2 

Yes � 8 

No � 17 

Don’t know � 2 

Changed from yes 

to no � 6; 

changed from no 

to yes � 1 

Correct answer “no” 

P � 0.15 Fischer’s exact 

test 

52.5 � 32 47 � 26 �5.8 � 28 The correct answer is 0%; 

all overoptimistic 

60 � 32 57.5 � 17.6 �4.2 � 28 All overoptimistic 

87 � 19 74.2 � 21 �6.7 � 26 All overoptimistic 

How long does the average 

person live with advanced ____ 

cancer (using the choices from the 

breast cancer sheet for example)? 

More than 3 years 18 14 �4 

About 2 years 6 11 5 

About 6 months 0 0 0 

Just a few weeks 1 0 �1 

Don’t know/NA 2 2 2 

Distress observed by interviewer, 

nurse, or oncologist 

No No 

Categorical variables Yes and No analyzed by Fischer’s exact test 

Numerical variables analyzed by Student’s t-test, unpaired; none significant 

Table 4 

Intent to Share the Information 

Will you share it with anyone? Yes � 20 

No � 6 

NA � 1 

If so, who? 

__ My family 

__ My oncologist 

__ My oncology nurse 

__ My primary care doctor 

__ Other ______ 

Was this patient information sheet 

helpful to you? 

All (family, ONC, PCP) � 14 

Family only � 2 

Oncologist � 12 

PCP � 14, one said “not 

PCP” 

Yes � 25 

No � 1, “Bummer” 

NA � 2 

NA � no answer; ONC � oncologist; PCP � primary care provider 


More realistic, but 2 people 

increased their expected 

length of survival 

time, often an overestimate; 7 and a recent meta-analysis 

showed that cancer physicians consistently overestimated 

prognosis by at least 30%. 43 Honest information respects the 

autonomy of a patient to make decisions based on what is 

known about the outcomes of such decisions. 44 Such information 

should not be forced on a patient, but the patient 

should be told that the information is available and that he or 

she has the right to accept or decline the information. 45 

When we started this project, colleagues were concerned 

about whether patients would want such information, that 

patients would be distressed by poor prognosis, that patients 

would give up hope, and that the procedures would take too 

much time. We also were concerned about the effect of giving 

such bad news on the provider, when prior research showed 

negative effects on the information-giver’s mood and affect 

from such encounters 46 and that doctors in general protect 

themselves by not giving bad news. 47 Completion of the 

decision aid was difficult for the interviewers, too. Some 

commented on how hard it was to give “bad” information 

about chance of cure and expected survival, even for patients 

they did not know. While patients may be more comfortable 

having advance directive discussions with a doctor they do 

not know rather than their oncologist, 48 it can still be hard 

for the provider. Surprisingly, it rarely took more than 20 

minutes to discuss the information including the tests since 

the information was preprinted. 

Patients vary in their approach to decision making, but the 

decisions should at least start with good information. Based on 



these preliminary findings, the piloted intervention is significant 

because it can lead to measurable impacts on knowledge about 

prognosis and appears to be judged helpful. We do not know the 

impact of full and truthful information on patient knowledge, 

decision making, hope, attendant choices about advanced medical 

directives, chemotherapy use, or hospice use. The next steps 

are to make the information available directly to patients on the 

Internet, which is in progress. The purpose is not to increase or 

decrease the use of palliative chemotherapy or hospice care; the 

lack of research into the decisions fully informed patients make 

precludes any such prediction. Since the intervention appears to 

be successful in this pilot trial, it will be tested in conjunction 

with standard care in a randomized clinical trial with measurement 

of quality of care, quality of life, and health-care cost 

outcomes. 

Acknowledgments: This research was supported by VCU 

School of Medicine Research Year Out, GO8 LM0095259 

from the National Library of Medicine (T. J. S., L. L., J. K.), 

and R01CA116227-01 (T. J. S.) from the National Cancer 

Institute. 

Appendix A 

Decision Aids 

Patient Name: ___ 

Date: ___/___/___ 

Lung Cancer Second Line Chemotherapy 

What is my chance of being alive at one year if I take 

chemotherapy, or do best supportive care such as hospice? 

Chemotherapy with a drug like docetaxel (Taxotere®) or 

pemetrexed (Alimta®) improves the chance of being alive at 

one year by 18 out of 100 people. With chemotherapy, 37 of 

100 people were alive at one year. Without chemotherapy, 11 

of 100 were alive. 

Patients receiving docetaxel (Taxotere®) chemotherapy lived 

an average of 7.5 months, versus 4.6 months if they did not take 

chemotherapy. In other words, they lived 2 to 3 months longer. 

If you are having cancer-related symptoms that limit your 

daily activities, the chances of being alive at one year are less 

than that described above. 

The numbers given here are what happens to the average 

person with this disease in this situation. Half the patients 

will do better than this, and half will do worse. Your 

situation could be better or worse. The numbers given for 

the chance of cure are very accurate. The numbers are 

given to help you with your own decision making. 

What is the chance of my cancer shrinking by half? 

About 6 of 100 people will have their cancer shrink by half. 

If you are having cancer-related symptoms that limit your 

daily activities, the chances are less than that described 

above. 

Chemotherapy 

No 

chemotherapy 

0% 20% 40% 60% 80% 100% 

Chance of being alive at one year 

Alive at 1 year 

Dead at 1 year 

What is the chance of my being cured by chemotherapy? 

In this setting, there is no chance of cure. The goal may 

change to controlling the disease and any symptoms for as 

long as possible. You may want to talk with your doctor about 

your own chances and goals of therapy. 

How long will chemotherapy make my cancer shrink, if it does? 

For all patients who did not get chemotherapy, the average time 

before the cancer grew was 7 weeks. For patients who got chemotherapy, 

the average time before the cancer grew was 11 weeks. 

What did chemotherapy do to quality of life? 

Chemotherapy helped reduce pain scores and did not make 

quality of life worse. 

What are the most common side effects? 

The most common side effects will vary with the type of treatment 

given. 

Some of the most common ones include the following: 

Mucositis (mouth sores). 

Nausea/vomiting; usually controllable. 

Alopecia (hair loss). 

Neutropenia (low white blood cell count) and infection requiring 

antibiotics. 

Neuropathy (numbness and pain in the hands and feet). 

Are there other issues that I should address at this time? 

Many people use this time to address a life review–what they have 

learned during life that they want to share with their families, and 

planning for events in the future like birthdays or weddings). 

Some people address spiritual issues. 

Some people address financial issues like a will. 

Some people address Advance Directives (Living Wills). 

For instance, if you could not speak for yourself, who would 

you want to make decisions about your care? 

If your heart stopped beating, or you stopped breathing, due to 

the cancer worsening, would you want to have resuscitation 

(CPR), or be allowed to die naturally without resuscitation? 

Some people use this time to discuss with their loved ones 

how they would like to spend the rest of their life. For 

instance, where do you want to spend your last days? Where 

do you want to die? 

Do you want to have hospice involved? 

These are all difficult issues, but important to discuss with 

your family and your health care professionals. 



1. Harrington SE, Smith TJ. The role of chemotherapy 

at the end of life: “when is enough, 

enough?” JAMA 2008;299(22):2667–2678. 

2. Koedoot CG, Oort FJ, de Haan RJ, Bakker 

PJ, de Graeff A, de Haes JC. The content and 

amount of information given by medical oncologists 

when telling patients with advanced cancer 

what their treatment options are. Palliative 

chemotherapy and watchful-waiting. Eur J Cancer 

2004;40(2):225–235. 

3. Sullivan AM, Lakoma MD, Matsuyama RK, 

Rosenblatt L, Arnold RM, Block SD. Diagnosing 

and discussing imminent death in the hospital: 

a secondary analysis of physician interviews. 

J Palliat Med 2007;10(4):882–893. 

4. Daugherty CK, Hlubocky FJ. What are terminally 

ill cancer patients told about their expected 

deaths? A study of cancer physicians’ 

self-reports of prognosis disclosure. J Clin Oncol 

2008;26(36):5988–5993. 

5. Wright AA, Zhang B, Ray A, Mack JW, Trice 

E, Balboni T, Mitchell SL, Jackson VA, Block SD, 

Maciejewski PK, Prigerson HG. Associations between 

end-of-life discussions, patient mental 

health, medical care near death, and caregiver 

bereavement adjustment. JAMA 2008;300(14): 

1665–1673. 

6. Zhang B, Wright AA, Huskamp HA, Nilsson 

ME, Maciejewski ML, Earle CC, Block SD, Maciejewski 

PK, Prigerson HG. Health care costs in 

the last week of life: associations with end-of-life 

conversations. Arch Intern Med 2009;169(5): 

480–488. 

7. Lamont EB, Christakis NA. Prognostic disclosure 

to patients with cancer near the end of 

life. Ann Intern Med 2001;134(12):1096–1105. 

8. Christakis N. Death Foretold: Prophecy and 

Prognosis in Medical Care. Chicago: University of 

Chicago Press; 1999. 

9. Swisher KN, Ulmer DL, Carpenter W, Mitchell 

RB, Wan TH, Dowdy M, Smith TJ. Patient 

indemnification as a way to involve patients in 

economic decision-making about their care: barriers 

in the current system. Proc ASCO 1996: 

939a. 

10. Siminoff LA, Gordon NH, Silverman P, 

Budd T, Ravdin PM. A decision aid to assist in 

adjuvant therapy choices for breast cancer. Psychooncology 

2006;15(11):1001–1013. 

11. Peele PB, Siminoff LA, Xu Y, Ravdin PM. 

Decreased use of adjuvant breast cancer therapy 

in a randomized controlled trial of a decision 

aid with individualized risk information. 

Med Decis Making 2005;25(3):301–307. 

12. Smith TJ. Tell it like it is. J Clin Oncol 

2000;18;3441–3445. 

13. Herth K. Abbreviated instrument to 

measure hope: development and psychometric 

evaluation. J Adv Nurs 1992;17(10):1251– 

1259. 

14. Higginson IJ, Donaldson N. Relationship 

between three palliative care outcome scales. 

Health Qual Life Outcomes 2004;2:68. 

15. Murray MA, Miller T, Fiset V, O’Connor A, 

Jacobsen MJ. Decision support: helping patients 

and families to find a balance at the end of life. 

Int J Palliat Nurs 2004;10(6):270–277. 

16. Woolf SH, Chan EC, Harris R, Sheridan SL, 

Braddock CH 3rd, Kaplan RM, Krist A, O’Connor 

AM, Tunis S. Promoting informed choice: transforming 

health care to dispense knowledge for 

decision making. Ann Intern Med 2005;143(4): 

293–300. 

17. O’Connor AM, Stacey D, Entwistle V, 

Llewellyn-Thomas H, Rovner D, Holmes-Rovner 

M, Tait V, Tetroe J, Fiset V, Barry M, Jones J. 

Decision aids for people facing health treatment 

or screening decisions. Cochrane Database Syst 

Rev 2003;(2):CD001431. 

18. Smith TJ, Dow LA, Virago E, Khatcheressian 

J, Lyckholm L, Matsuyama R. Giving honest 

information to patients with advanced cancer 

maintains hope. Oncologist (Williston Park) 

24(6):521–525. 

19. Tulsky JA, Chesney MA, Lo B. How do 

medical residents discuss resuscitation with 

patients? J Gen Intern Med 1995;10(8):436– 

442. 

20. Smith TJ, Desch CE, Hackney MH, Shaw 

JE. How long does it take to get a “do not 

resuscitate” order? J Palliat Care 1997;13(1):5–8. 

21. Whelan TJ, Loprinzi CL. Physician/patient 

decision aids for adjuvant therapy. J Clin Oncol 

2005;23:1627–1630. 

22. Whelan TM, O’Brien MA, Villasis-Keever 

M, et al. Impact of Cancer-Related Decision Aids. 

Evidence Report/Technology Assessment Number 

46. (Prepared by McMaster University under 

contract no. 290-97-0017.) AHRQ Publication 02- 

E004, Rockville, MD: Agency for Healthcare Research 

and Quality; 2002. 

23. Cassileth BR, Soloway MS, Vogelzang NJ, 

et al. Patients’ choice of treatment in stage D 

prostate cancer. Urology 1989;33(suppl 5):S57– 

S62. 

24. Chadwick DJ, Gillatt DA, Gingell JC. Medical 

or surgical orchidectomy: The patients’ 

choice. BMJ 302:572, 1991. 

25. Elit LM, Levine MN, Gafni A, et al. Patients’ 

preferences for therapy in advanced epithelial 

ovarian cancer: Development, testing, and application 

of a bedside decision instrument. Gynecol 

Oncol 62:329–335, 1996. 

26. Fiset V, O’Connor AM, Evans W, et al. 

Development and evaluation of a decision aid 

for patients with stage IV non-small cell lung 

cancer. Health Expect 3:125–136, 2000. 

27. Brundage MD, Feldman-Steward D, 

Cosby R, et al. Cancer patients’ attitudes toward 

treatment options for advanced non-small cell 

lung cancer: Implications for patient education 

and decision support. Patient Educ Couns 45: 

149–157, 2001. 

28. Brundage MD, Feldman-Stewart D, 

Cosby R, et al. Phase I study of a decision aid 

for patients with locally advanced non-smallcell 

lung cancer. J Clin Oncol 19:1326–1335, 

2001. 

29. Chiew KS, Shepherd H, Vardy J, Tattersall 

MH, Butow PN, Leighl NB. Development 


and evaluation of a decision aid for patients 

considering first-line chemotherapy for metastatic 

breast cancer. Health Expect 2008;11(1): 

35–45. 

30. Mackillop WJ, Stewart WE, Ginsburg AD, 

Stewart SS. Cancer patients’ perceptions of their 

disease and its treatment. Br J Cancer 1988;58: 

355–358. 

31. Chow E, Andersson L, Wong R, Vachon 

M, Hruby G, Franssen E, et al. Patients with 

advanced cancer: a survey of the understanding 

of their illness and expectations from palliative 

radiotherapy for symptomatic metastases. 

Clin Oncol (R Coll Radiol) 2001;13:204– 

208. 

32. Ganti AK, Lee SJ, Vose JM, Devetten MP, 

Bociek RG, Armitage JO, Bierman PJ, Maness 

LJ, Reed EC, Loberiza FR Jr. Outcomes after 

hematopoietic stem-cell transplantation for 

hematologic malignancies in patients with or 

without advance care planning. J Clin Oncol 

2007;25(35):5643–5648. 

33. Lee SJ, Fairclough D, Antin JH, Weeks JC. 

Discrepancies between patient and physician 

estimates for the success of stem cell transplantation. 

JAMA 2001;285(8):1034–1038. 

34. Lee SJ, Loberiza FR, Rizzo JD, Soiffer RJ, 

Antin JH, Weeks JC. Optimistic expectations and 

survival after hematopoietic stem cell transplantation. 

Biol Blood Marrow Transplant 2003;9: 

389–396. 

35. Weeks JC, Cook EF, O’Day SJ, Peterson 

LM, Wenger N, Reding D, et al. Relationship 

between cancer patients’ predictions of prognosis 

and their treatment preferences. JAMA 1998; 

279(21):1709–1714. 

36. Earle CC, Neville BA, Landrum MB, Ayanian 

JZ, Block SD, Weeks JC. Trends in the aggressiveness 

of cancer care near the end of life. 

J Clin Oncol 2004;22(2):315–321. 

37. Matsuyama R, Reddy SN, Smith TJ. Why 

do patients choose chemotherapy near the end 

of life? J Clin Oncol 2006;24:1–7. 

38. Dufresne A, Pivot X, Tournigand C, Facchini 

T, Altweegg T, Chaigneau L, De Gramont A. 

Impact of chemotherapy beyond the first line in 

patients with metastatic breast cancer. Breast 

Cancer Res Treat 2008;107(2):275–279. 

39. Kirk P, Kirk I, Kristjanson LJ. What do 

patients receiving palliative care for cancer 

and their families want to be told? A Canadian 

and Australian qualitative study. BMJ 2004; 

328(7452):1343. 

40. Emanuel LL, Ferris FD, von Gunten CF, 

Von Roenn J. EPEC-O: Education in Palliative and 

End of Life Care for Oncology. Chicago: EPEC 

Project; 2005. 

41. Field MJ, Cassel CK. Approaching Death: 

Improving Care at the End of Life. Washington 

DC: National Academy Press; 1997:59–64. 

42. Huskamp HA, Keating NL, Malin JL, 

Zaslavsky AM, Weeks JC, Earle CC, Teno JM, 

Virnig BA, Kahn KL, He Y, Ayanian JZ. Discussions 

with physicians about hospice among patients 



with metastatic lung cancer. Arch Intern Med 

2009;169(10):954–962. 

43. Glare P, Virik K, Jones M, Hudson M, Eychmuller 

S, Simes J, et al. A systematic review of 

physicians’ survival predictions in terminally ill cancer 

patients. BMJ 2003;327:195–200. 

44. Smith T, Swisher K. Telling the truth about 

terminal cancer. JAMA 1998;279(21):1746–1748. 

45. Beauchamp T, Childress J. Principles of 

Biomedical Ethics, 5th ed. New York: Oxford University 

Press; 2001. 

46. Panagopoulou E, Mintziori G, Montgomery 

A, Kapoukranidou D, Benos A. Concealment of 

information in clinical practice: is lying less stressful 

than telling the truth? J Clin Oncol 2008;26(7): 

1175–1177. 

47. Fallowfield L, Jenkins V. Communicating 

sad, bad, and difficult news in medicine. Lancet 

2004;363(9405):312–319. 

48. Dow LA, Matsuyama RK, Ramakrishnan V, 

Kuhn L, Lamont EB, Lyckholm L, Smith TJ. Paradoxes 

in advance care planning: the complex relationship 

of oncology patients, their physicians, and advance 

medical directives. J Clin Oncol 2010;28(2):299–304.

Evaluating the “Good Death” Concept from Iranian Bereaved Family

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