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2. I1 = incidence in the exposed P1 = proportion of population exposed I0 = incidence in the nonexposed R0 = proportion of the population nonexposed RR = relative risk We know that: I = I1P1 + I0P0 Ie = (RR)(I0), so that I = (RR)(I0)(P1) + I0P0, and From the problem, I = .001 I0 = I ————— (RR)(P1) + P0 P1 = .2; P0 = .8 RR = 2.11 (combining LCC & VLCC into 1 group) 0.001 I0 = ————— = 0.0008 2.11(.2)+.8 I – I0 0.001-0.0008 Population Attributable Risk % = ——— = —————— = 20% I 0.001 (For a review of the 20 years of epidemiologic studies stimulated by this one, see Greenland S, Sheppard AR, Kaune WT, Poole C, Kelsh MA. A pooled analysis of magnetic fields, wire codes, and childhood leukemia. Epidemiology 2000;11:624-634.) a. Study design - a case control study with prevalent cases. b. Selection bias: i) Non-response--about 29% of the sample did not return completed questionnaires. If nonrespondents were disproportionately distributed so that the nonresponse rates among MI nonusers of OCs or non-MI users of OC were higher than the other nonresponse rates, then the odds ratio would overstate the true association. _____________________________________________________________________________________________ www.epidemiolog.net © Victor J. Schoenbach 10. Sources of error - Assignment solutions - 330 rev. 9/6/1999, 3/30/2001, 5/8/2001
3. ii) Selective survival--if OC users and nonusers had different fatality rates from MI, then the prevalent (surviving) MI subjects would not provide an accurate estimate of OC use among women who later develop MI. If the fatal MI rate is higher among nonusers of OC, then the odds ratio observed would overstate the true association. c. Misclassification of the outcome measure is likely, since: i) MI may go undetected ("silent MI") ii) MI may not be diagnosed, despite symptoms (due to, for example, lack of sensitivity of diagnostic tests used) iii) MI history may be "denied" or mistaken, though for nurses one expects greater accuracy of reporting. No check of hospital records appears to have been made. Probably the true prevalence will be understated. Misclassification of the exposure measure is also likely, particularly concerning time periods and duration of OC use. Memory in this case is unverified by pharmacy or physician records. Furthermore, the differences among types of OC preparations have not been noted. The above limitations do not by any means invalidate the study, nor should the investigators necessarily have attempted to collect additional data. But it is important to be aware of the limitations in interpreting the data and in reconciling results with other investigations. a. Detection bias, in the sense in which Horwitz and Feinstein have applied the term to studies of endometrial cancer and exogenous estrogen, refers to a distortion in the observed proportion of estrogen users among women diagnosed as having endometrial cancer. The distortion in the case would result from the allegedly greater likelihood of diagnostic testing for endometrial cancer in women who take estrogens. The series of events envisioned is: women who take estrogen tend to have vaginal bleeding, prompting them to see their doctor, who then performs a diagnostic procedure (dilitation and curretage ["D&C"]). If an asymptomatic cancer is present, it will come to medical attention. A similar cancer present in a woman not receiving estrogens would go undetected. So the additional diagnostic attention given to the women taking estrogens leads, according to Horwitz and Feinstein, to additional detection of asymptomatic cancers. The end result is that in any series of endometrial cancer cases, the proportion of estrogen users is artificially inflated. b. "Detection bias", as described above, would tend to overstate a truly positive association between estrogen use and endometrial cancer, because by artificially inflating the proportion of estrogen users among endometrial cancer cases, the difference between cases and controls would become more marked. c. "Detection bias". in the above sense, is a form of selection bias, since it deals with the selection or ascertainment of cases into the study population. It is true that there is a form of misclassification at work, in that women with asymptomatic endometrial cancer are going unrecognized as cases – and one or two of them might conceivably appear among the control group of a study population. Processes that influence who becomes part of the study population lie in the realm of selection bias. The misclassification of a possible control or two in a study population could cause information bias, but only to a trivial degree. So it _____________________________________________________________________________________________ www.epidemiolog.net © Victor J. Schoenbach 10. Sources of error - Assignment solutions - 331 rev. 9/6/1999, 3/30/2001, 5/8/2001
Page 1 and 2:
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
Page 285 and 286: But excluding an explanation based
Page 287 and 288: Strength of the association Pronou
Page 289 and 290: Experimental evidence Certain types
Page 291 and 292: To issue an indictment, the grand j
Page 293 and 294: 10. Sources of error A systematic f
Page 295 and 296: the data in the table. (Note that t
Page 297 and 298: associations. So the same terms may
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: Sources of error - Assignment solut
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 and 346: A hypothetical example (with apolog
Page 347 and 348: Questions to ask: There are many as
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
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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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