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3,154 male managers and white collar employees w/o CHD at baseline Figure 1 Western Collaborative Group Study 8-1/2 year follow-up 2,648 enrolled and followed up 1,307 Type A 1,341 Type B 178 new cases 1,129 no CHD 79 new cases 506 lost to 1,262 follow-up no CHD 8 1/2 years Table 1 Relationship of CHD to Behavior Pattern Behavior pattern A B Total CHD cases 178 79 257 No manifest CHD 1,129 1,262 2,391 Total 1,307 1,341 2,648 Since these data come from a cohort study, we would analyze them by estimating the incidence of CHD for the Type A and Type B groups. Even though the risk period for CHD extends beyond the period of observation, we will use cumulative incidence (CI) for simplicity. In these data, the CI is 178/1307 = 0.14 for the Type A group, and 79/1341 = 0.06 for the Type B group. The relative risk (risk ratio, cumulative incidence ratio) is therefore 0.14/0.06 = 2.3 _____________________________________________________________________________________________ www.epidemiolog.net, © Victor J. Schoenbach 2000 11. Multicausality: Confounding - 340 rev. 10/28/2000, 11/2/2000, 5/11/2001
Questions to ask: There are many aspects of the design and conduct of this study that we would want to inquire about. For example: What were the criteria for classifying participants as Type A or Type B? How many participants were lost to follow-up? How was CHD defined and diagnosed? Were the physicians who determined whether a participant was a new case or not aware of the participant's behavior type? But since our topic today is confounding, we are most interested in the question: Do the Type A and Type B groups differ in other factors that might have affected their observed CHD rates? or, equivalently, Are there factors other than behavior pattern that may have been responsible for the observed rates? (It might be interjected here that the same question would be relevant whether a difference between Type A and Type B had been observed or not). Hypothetical scenario Probably most of you know that in the Western Collaborative Group Study, no other factors seemed to explain the difference in CHD incidence between Type A and Type B groups. So here we will depart from the actual study in order to create a scenario in which the difference in the observed incidence for Type A and Type B participants is actually due to differences in cigarette smoking. Suppose we had obtained the data in the Table 1. How could we see whether the difference in incidence between Type A and Type B groups should be attributed to differences in smoking rather than to behavior type? The traditional and most common approach to answering this question is to break down or stratify the data by cigarette smoking status of the participants. Table 2 shows the results of such a stratified analysis (with hypothetical data). _____________________________________________________________________________________________ www.epidemiolog.net, © Victor J. Schoenbach 2000 11. Multicausality: Confounding - 341 rev. 10/28/2000, 11/2/2000, 5/11/2001
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Understanding the Fundamentals of E
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Preface Introductory epidemiology c
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Table of Contents Chapter (in Acrob
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1. Introduction Definition, charact
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problems (Schwartz and Carpenter, 1
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HIV - a new or newly-recognized vir
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d = principal investigator's degree
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Analytic studies typically involve
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Observational sciences especially a
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McGavran EG. What is public health?
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2. An evolving historical perspecti
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Time line for the history of public
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as a one-room bacteriology laborato
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− Health surveys − Research fun
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his scientific peers of the correct
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Kuhn, Thomas S. The structure of sc
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3. Studying populations - basic dem
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The demographic balancing equation
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ates. Similarly, bulges in the repr
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When the baby boom cohort retires 1
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The pervasiveness, strength, viciou
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Total fertility rate (TFR) Standard
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Life expectancy The technique, of u
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Excerpt from the U.S. 1993 abridged
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At the other end of the life table,
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to change, hopefully to decline. If
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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
Page 295 and 296: the data in the table. (Note that t
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Page 299 and 300: "Negative bias" - The observed meas
Page 301 and 302: Classifying sources of bias Inspite
Page 303 and 304: The study population is a subset of
Page 305 and 306: This estimate of the prevalence of
Page 307 and 308: unmatched control groups will presu
Page 309 and 310: The odds ratio for this table is 3.
Page 311 and 312: Selection bias in cohort studies Se
Page 313 and 314: Information bias Information bias r
Page 315 and 316: following chapter). The concept of
Page 317 and 318: P h y s i c i a n A Comparison of d
Page 319 and 320: item). Alpha values of 0.80 are are
Page 321 and 322: Hypothetical scenario showing effec
Page 323 and 324: presumably classified as informatio
Page 325 and 326: Appendix 1 Formulas to see the effe
Page 327 and 328: Algebraically, _ Pr(E|D') = — the
Page 329 and 330: Gregorio, David L.; James R. Marsha
Page 331 and 332: Sources of error - Assignment 1. Th
Page 333 and 334: Alpha (α) is the probability by wh
Page 335 and 336: Sources of error - Assignment solut
Page 337 and 338: 3. ii) Selective survival--if OC us
Page 339 and 340: a. Flow diagram: 1960-1962 1967-196
Page 341 and 342: Introduction 11. Multicausality: Co
Page 343 and 344: estimate of what the outcomes would
Page 345: A hypothetical example (with apolog
Page 349 and 350: Confounding arises from unequal dis
Page 351 and 352: CHD Incidence by Behavior Pattern a
Page 353 and 354: In this way, the 1,129 Type A's wit
Page 355 and 356: exposure. If we assume a baseline r
Page 357 and 358: Further insight can be gained by co
Page 359 and 360: Table 7a Colon cancer and drinking
Page 361 and 362: atio of the observed number of expo
Page 363 and 364: ural-specific exposure prevalences,
Page 365 and 366: eing infected, and the more of them
Page 367 and 368: studies deal with a delimited geogr
Page 369 and 370: University of Massachusetts at Amhe
Page 371 and 372: similarly distributed in cases and
Page 373 and 374: "There have been many important ste
Page 375 and 376: MAIN POINTS Confounding is a disto
Page 377 and 378: Greenland, S. and Neutra R.: Contro
Page 379 and 380: Appendix The following discussion i
Page 381 and 382: i n1i wi = ----- If exposure is unr
Page 383 and 384: Multicausality: Confounding - Assig
Page 385 and 386: 4. The following table, published i
Page 387 and 388: 1. Multicausality: Confounding - As
Page 389 and 390: link between HCS and MI to ensure t
Page 391 and 392: Multicausality 12. Multicausality:
Page 393 and 394: Interdependent effects The precedin
Page 395 and 396: 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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