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© 1999, 2000 Victor J. Schoenbach Unless otherwise indicated, the text and diagrams in this work belong to the copyright owner above. For reprint permission (royalty-free for noncommercial use by nonprofit, accredited, educational organizations), please write to: Victor J. Schoenbach, Ph.D. Department of Epidemiology University of North Carolina School of Public Health Chapel Hill, NC 27599-7400 USA Victor_Schoenbach@unc.edu Permission to reprint material copyrighted by others and used here by their permission must be obtained directly from them. August 1999, 2000 Chapel Hill, North Carolina
Preface Introductory epidemiology courses are often referred to as "methods" courses, and many students come to them hoping to learn the methods that have made epidemiology so important. Certainly methods are an essential aspect of the field, and this text covers the usual complement. But especially for the newcomer, the critical need is to learn how epidemiologists think about health and the factors that affect it, and how epidemiologists approach studying them. Very few methods are unique to epidemiology. "Epidemiologic thinking" is its essence. Therefore, for me the central objective of an introductory course has been to explain the concepts and perspectives of the field. For nearly 20 years I have had the privilege of teaching the introductory epidemiology course for epidemiology majors at the University of North Carolina School of Public Health and the special pleasure that derives from teaching students who have sought epidemiology out rather than come to learn it only as a school requirement. I have also had the honor of being entrusted by my colleagues with the responsibility for introducing our students to epidemiologic concepts and methods. Over the years I have written out extensive lecture notes, initially in response to requests from course participants and subsequently to develop my own understanding. Not all course participants have appreciated them, but I have received sufficient positive feedback and expressions of interest from graduates who have gone on to teach their own epidemiology courses that I have decided to recast them as an "evolving text". I use the term "evolving" because I continue to clarify, develop, refine, correct, and, I hope, improve. Regarding it as an evolving text is also my excuse for the fact that the material is not ready for formal publication. Moreover, unlike a published text, this volume does not claim to be authoritative – nor even thoroughly proofread. As an evolving work, its further development has always taken priority over appearance – and, it must be admitted, occasionally also over accuracy. * Although the word processing is nearly all my own, the content is certainly not. Besides the extensive development and exposition of epidemiologic concepts and methods from courses and publications by others, I have had the good fortune to study with and learn from outstanding epidemiologists and biostatisticians, among them the late John Cassel, Gerardo Heiss, Barbara Hulka, Michel Ibrahim, Sherman James, Bert Kaplan, David Kleinbaum, Gary Koch, Lawrence Kupper, Hal Morgenstern, Abdel Omran, the late Ralph Patrick, Dana Quade, David Savitz, Carl Shy, the late Cecil Slome, H.A. Tyroler, and Edward Wagner. * Important errata, as I learn about them, are posted on a site on the World Wide Web (http://www.epidemiolog.net/). _____________________________________________________________________________________________ www.epidemiolog.net, © Victor J. Schoenbach 1999, 2000 rev. 9/26/1999, 8/20/2000, 3/9/2001 1. Epidemiology Definition, functions, and characteristics - 1
Page 1: Understanding the Fundamentals of E
Page 5 and 6: Table of Contents Chapter (in Acrob
Page 7 and 8: 1. Introduction Definition, charact
Page 9 and 10: problems (Schwartz and Carpenter, 1
Page 11 and 12: HIV - a new or newly-recognized vir
Page 13 and 14: d = principal investigator's degree
Page 15 and 16: Analytic studies typically involve
Page 17 and 18: Observational sciences especially a
Page 19 and 20: McGavran EG. What is public health?
Page 21 and 22: 2. An evolving historical perspecti
Page 23 and 24: Time line for the history of public
Page 25 and 26: as a one-room bacteriology laborato
Page 27 and 28: − Health surveys − Research fun
Page 29 and 30: his scientific peers of the correct
Page 31 and 32: Kuhn, Thomas S. The structure of sc
Page 33 and 34: 3. Studying populations - basic dem
Page 35 and 36: The demographic balancing equation
Page 37 and 38: ates. Similarly, bulges in the repr
Page 39 and 40: When the baby boom cohort retires 1
Page 41 and 42: The pervasiveness, strength, viciou
Page 43 and 44: Total fertility rate (TFR) Standard
Page 45 and 46: Life expectancy The technique, of u
Page 47 and 48: Excerpt from the U.S. 1993 abridged
Page 49 and 50: At the other end of the life table,
Page 51 and 52: to change, hopefully to decline. If
Page 53 and 54:
Thought question: Professors typica
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Descriptive studies and surveillanc
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4. The Phenomenon of Disease Concep
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Classification is the foundation As
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As we gain more sophisticated under
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To recapitulate the above discussio
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Classifying cause of death Since mo
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Laboratory and other objectively me
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distribution but are not diabetic (
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program in the general population,
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Infectious disease Incubation "time
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the direct effects of carcinogenic
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patient outcomes. The design and ev
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detected by screening will appear b
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Morrison, Alan S. Screening in chro
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e relevant. Compromises might be fo
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educational attainment by the numbe
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Distributions - the fuller picture
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Per capita income: Should health ca
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Deaths of children < 1 year of age
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No. of persons with senile dementia
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Incidence Immigration Relationship
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Examples: arthritis, cholelithiasis
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Cohort - entrance into the populati
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Sample calculation: 200 people free
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• The units of time must be state
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Calculation of person-time in a coh
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Original Calculation of person-time
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average risk for a member of the co
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However: Nurses: 601 cases/89,538 w
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treated. If the rate at which patie
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Incidence and prevalence in a popul
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small prevalence. So if the seropre
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Appendix on weighted averages Becau
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Appendix on exponents and logarithm
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and epidemiology. The notation ln(x
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3. For the following hypothetical d
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c. ________________ 6 fatalities /
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Measuring disease and exposure - As
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5. a. Flow Diagram Population of si
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New cases Q ID = ------------------
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Overview 6. Standardization of rate
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differences between the groups in f
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∑ (stratum-specific rates × stan
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especially important when comparing
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copy this cell to the other columns
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where the RR k are the stratum-spec
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and the observed number of deaths i
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Directly standardized rate for A =
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5,022 Indirectly standardized = —
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the situation among older age group
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Appendix on Standardized Mortality
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across age strata, then the damage
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d. How would you feel about the con
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Cases per 100,000 Mean annual incid
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d. dt Indirectly standardized rates
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The second ingredient for an standa
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Group US rate /100,000 County pop.
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Crude rates are comparable because
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The “Big Picture” 7. Relating r
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CHD and oral contraceptives in Brea
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Some basic measures Before diving i
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esophageal cancer? First, if we did
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ought in here as well. When we cont
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Exposure Disease Yes No Total Yes a
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Relation of the odds ratio to the r
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the case-control study estimates th
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If the data had come from a cross-s
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Excess risk = CIR - 1 = 1.0 (i.e.,
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strength of the relationship (e.g.,
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drawn on ordinary graph paper. Howe
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Measures of Impact Concept Relative
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Attributable risk proportion The AR
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show that a large proportion (i.e.,
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Incidence Diagrammatic representati
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1. high frequency of disease 2. pow
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With this diagram and notation we c
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vaguer) “attributed to”. Incide
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Summary There are three categories
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CIR can be directly estimated if th
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I0 I = ——————— 1 + P1
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PD|e PE (RR - 1) PD|e PE (RR - 1) P
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Walter, Stephen D. Choice of effect
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a. Quit rates were measured as the
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1. Definitions: Relating risk facto
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There is an extremely strong associ
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8. Analytic study designs The archi
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Progression of types of studies In
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Cross-sectional A cross-sectional s
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Follow-up studies Along with case-c
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of interest community-level influen
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ecological approach may be the appr
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What measures can be estimated from
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Ability to estimate risk Ascertainm
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individuals, this relationship does
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Odds Ratio (OR) Exposure odds in ca
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Simple randomization - assignment o
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The relationship of an attribute to
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(a / m1) / (b / m1) ad ORe (Exposur
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Validity The validity of a case-con
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Since the strategic advantage of th
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c = f0N1 = number of exposed noncas
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experienced the event under study.
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approach, the odds ratio computatio
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Exposure Yes No Total Cases A B M1
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Miettinen, Olli S. The clinical tri
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Mantel, N. and Haenszel, W.: Statis
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Peto, R., Pike, M.C., Armitage, P.,
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4. The authors state that "There we
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5. In contrast to many earlier inve
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In this study the use of hospitaliz
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Follow-up must be longer for any gi
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While acknowledging the very import
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world. Even as late as the 1950's,
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Causal Inference Direct observation
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data on the effects of radiation on
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But excluding an explanation based
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Strength of the association Pronou
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Experimental evidence Certain types
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To issue an indictment, the grand j
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10. Sources of error A systematic f
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the data in the table. (Note that t
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associations. So the same terms may
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"Negative bias" - The observed meas
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Classifying sources of bias Inspite
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The study population is a subset of
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This estimate of the prevalence of
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unmatched control groups will presu
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The odds ratio for this table is 3.
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Selection bias in cohort studies Se
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Information bias Information bias r
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following chapter). The concept of
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P h y s i c i a n A Comparison of d
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item). Alpha values of 0.80 are are
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Hypothetical scenario showing effec
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presumably classified as informatio
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Appendix 1 Formulas to see the effe
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Algebraically, _ Pr(E|D') = — the
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Gregorio, David L.; James R. Marsha
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Sources of error - Assignment 1. Th
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Alpha (α) is the probability by wh
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Sources of error - Assignment solut
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3. ii) Selective survival--if OC us
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a. Flow diagram: 1960-1962 1967-196
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Introduction 11. Multicausality: Co
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estimate of what the outcomes would
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A hypothetical example (with apolog
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Questions to ask: There are many as
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Confounding arises from unequal dis
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CHD Incidence by Behavior Pattern a
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In this way, the 1,129 Type A's wit
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exposure. If we assume a baseline r
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Further insight can be gained by co
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Table 7a Colon cancer and drinking
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atio of the observed number of expo
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ural-specific exposure prevalences,
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eing infected, and the more of them
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studies deal with a delimited geogr
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University of Massachusetts at Amhe
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similarly distributed in cases and
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"There have been many important ste
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MAIN POINTS Confounding is a disto
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Greenland, S. and Neutra R.: Contro
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Appendix The following discussion i
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i n1i wi = ----- If exposure is unr
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Multicausality: Confounding - Assig
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4. The following table, published i
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1. Multicausality: Confounding - As
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link between HCS and MI to ensure t
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Multicausality 12. Multicausality:
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Interdependent effects The precedin
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epidemiologic data. Put simply, the
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BP 0 These two lines are parallel;
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ean intake and B representing genet
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Fourth, proportion of disease attri
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Incidence and incidence ratios of f
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% with evidence of impaired red blo
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cancer rate among the light smokers
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Age standardized lung cancer rates
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R11 - R00 = R10 - R00 + R01 - R00 (
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and R111 = R100 × R010 × R001 / (
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R110 = R100 × R010 / R000 (multipl
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Risk ln(risk) 0.19 -1.67 0.13 -2.01
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have clinical or public health sign
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Baseline Characteristics Associated
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Weiss, Noel S. Accounting for the m
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A. Synergism apparently exists in t
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5. Walker (International Journal of
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(or equivalently, the rate for pers
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impact. Synergism in this sense imp
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13. Multicausality - analysis appro
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very far on instruments but needs t
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Summarizing the relationships Often
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We saw in the chapter on effect mod
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So the multiplicative model for joi
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misleading summary. These circumsta
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w1 CID1 + w2 CID2 Summary CID = —
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c d bcn0 + adn1 Var(ln(CIR)) ≈
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Kleinbaum, Kupper, and Morgenstern;
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1 + 1 + 1 3 ORMH = —————
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Risk = R00 + Obesity effect Obesity
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two (or more) factors that "interac
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OR = exp(β3) β3 is the difference
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Other regression models [Optional f
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Know the epidemiologic meaning of t
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14. Data analysis and interpretatio
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and questionable data. Monitoring p
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presence of particular proteins (e.
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4. Count - the number of entities,
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Algorithms - A procedure that uses
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Imputation causes the least distort
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Prenatal exposure to diethylstilbes
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Possible outcomes (Colors of pairs
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In a strict decision-making mode, t
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C = area between -z and +z D = area
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two-tailed 5% significance level).
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If the p-value is not small (i.e.,
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OR we observed was greater than 1.0
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Large trials (e.g., 250 deaths) Tru
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The concept behind the confidence i
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Formulas for confidence intervals f
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Woolf SH, Battista RN, Anderson GM,
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Stallones, Reuel A. The use and abu
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C - Clustered observations, which d
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For example, suppose that n, the sa
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* See previous table Margin of erro
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Even in randomized trials problems
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Pr(H|T) is therefore a function of
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That information gives you pause. C
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Part II 1. In table 2 from the Rose
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If the OR for Current OC use were t
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6. There are no interaction (produc
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24. Submit manuscript from previous
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Cooperative agreement A cooperative
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Assurances by the applicant and her
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The new element is the grant award.
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1. What needs to be done? 2. How lo
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Postaward Final report What to do
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2. maintain close supervision of th
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Publication Epidemiology Working Gr
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Weed, Douglas L. Between science an
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1.3 Specific Objectives of Data Man
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engender a basic conflict between t
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1.4.2 Integration Integrate the dat
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2. Consistency of the protocol's im
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2. Coding of a sample of data forms
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2.3 The steps in the Editing proces
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Although it is rare to use more tha
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The first stages of data analysis s
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have less power to exclude the stat
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greater nonresponse by heavy drinke
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Imputation is then carried out as f
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(normal) distribution in the popula
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Appendix **************************
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___________________________________
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Public health approach The public h
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health, particularly at the world l
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Causative or preventive agents Scur
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Human behavior is also biology The
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crowded, under-resourced urbanized
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Bibliography Annas, George J.; Leon
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18. Overview and Conclusion A look
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Epidemiology is expanding In recen
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More reliance on "soft money" - res
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__________ This is not a new challe
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