Evaluating the “Good Death” Concept from Iranian Bereaved Family

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
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