Epidemiology Kept Simple Chapter 12 5202021 Error in

Epidemiology Kept Simple Chapter 12 5/20/2021 Error in Epidemiologic Research 1

§ 12. 1 Introduction • View analytic studies as exercises in measuring association between exposure & disease • All such measurements are prone to error • We must understand the nature of measurement error if we are to avoid train wrecks* * a disaster or failure, especially one that is unstoppable or unavoidable 5/20/2021 2

Parameters and Estimates Understanding measurement error begins by distinguishing between parameters and estimates Parameters – Error-free – Hypothetical – Measure effect Statistical estimates – Error-prone – Calculated – Measure association Epidemiologic statistics are mere estimates of the parameters they wish to estimate. 5/20/2021 3

Target Analogy Let Φ represent the true relative risk for an exposure and disease in a population Replicate the study many times How closely does an estimate from a given study reflect the true relative risk (parameter)? 5/20/2021 4

Target Analogy 5/20/2021 5

Random Error (Imprecision) • Balanced “scattering” • To address random error, use – confidence intervals – hypothesis tests of statistical significance 5/20/2021 6

Confidence Intervals (CI) • Surround estimate with margin of error • Locates parameter with given level of confidence (e. g. , 95% confidence) • CI width quantifies precision of the estimate (narrow CI precise estimate) 5/20/2021 7

Mammagraphic Screening and Breast Cancer Mortality • Exposure ≡ mammographic screening • Disease ≡ Breast cancer mortality • First series (studies 1 – 10): women in their 40 s • Second series (studies 11 – 15): women in their 50 s 5/20/2021 8

Hypothesis Tests • Start with this null hypothesis H 0: RR = 1 (no association) • Use data to calculate a test statistic • Use the test statistic to derive a Pvalue • Definition: P-value ≡ probability of the data, or data more extreme, assuming H 0 is true • Interpret P-value as a measure of evidence against H 0 (small P gives one reason to doubt H 0) 5/20/2021 R. A. Fisher 9

Conventions for Interpretation • Small P-value provide strong evidence against H 0 (with respect to random error explanations only!) • By convention – P ≤. 10 is considered marginally significant evidence against H 0 – P ≤. 01 is considered highly significant evidence against H 0 • Do NOT use arbitrary cutoffs (“surely, god loves P =. 06”) R. A. Fisher 5/20/2021 10

Childhood SES and Stroke Factor RR P-value Crowding < 1. 5 = 0. 4 1. 5 – 2. 49 = 1. 0 (ref. ) 2. 5 – 3. 49 = 0. 6 3. 5 = 1. 0 trend P = 0. 53 Tap water 0. 73 P = 0. 53 Toilet type flush/not shared = 1. 3 flush/shared = 1. 0 (referent) no flush = 1. 0 trend P = 0. 67 Ventilation good = 1. 0 (ref. ) fair = 1. 7 poor = 1. 7 trend P = 0. 08 Cleanliness good= 1. 1 fair = 1. 0 (ref. ) poor = 0. 5 trend P = 0. 07 (persons/room) 5/20/2021 Source: Galobardes et al. , Epidemiologic Reviews, 2004, p. 14 Nonsignif. evidence against H 0 Marginally significant evidence against H 0 11

Systematic Error (Bias) • Bias = systematic error in inference (not an imputation of prejudice) • Amount of bias • Direction of bias –Toward the null (under-estimates risks or benefits) –Away from null (overestimates risks or benefits) 5/20/2021 12

Categories of Bias • Selection bias: participants selected in a such a way as to favor a certain outcome • Information bias: misinformation favoring a particular outcome • Confounding: extraneous factors unbalanced in groups, favoring a certain outcome 5/20/2021 13

Examples of Selection Bias • • • Berkson’s bias Prevalence-incidence bias Publicity bias Healthy worker effect Convenience sample bias Read about it: pp. 229 – 231 5/20/2021 14

Examples of Information Bias • Recall bias • Diagnostic suspicion bias • Obsequiousness bias • Clever Hans effect • Read about it on page 231 5/20/2021 16

Memory Bias • Memory is constructed rather than played back • Example of a memory bias is The Misinformation Effect ≡ memory bias that occurs when misinformation affects people's reports of their own memory • False presuppositions, such as "Did the car stop at the stop sign? " when in fact it was a yield sign, will cause people to misreport actually happended a Loftus, E. F. & Palmer, J. C. (1974). Reconstruction of automobile destruction. Journal of Verbal Learning and Verbal Behaviour, 13, 585 -589. 5/20/2021 17

Recall Bias in Case. Control Studies 5/20/2021 18

Differential & Nondifferential Misclassification • Non-differential misclassification: groups equally misclassified • Differential misclassification: groups misclassified unequally 5/20/2021 19

Nondifferential and Differential Misclassification Illustrations 5/20/2021 20

Confounding • A distortion brought about by extraneous variables • From the Latin meaning “to mix together” • The effects of the exposure gets mixed with the effects of extraneous determinants 5/20/2021 22

Properties of a Confounding Variable • Associated with the exposure • An independent risk factor • Not in causal pathway 5/20/2021 23

Example of Confounding E ≡ Evacuation Died Survived Total method Helicopter 64 136 200 Road 260 840 1100 D ≡ Death following auto • R 1 = 64 / 200 =. 3200 accident • R 0 = 260 / 1100 =. 2364 • RR =. 3200 /. 2364 = 1. 35 • Does helicopter evacuation really increase the risk of death by 35%? Surely you’re joking! • Or is the RR of 1. 35 confounded? 5/20/2021 25

Serious Accidents Only (Stratum 1) • • Died Survived Total Helicopter 48 52 100 Road 60 40 100 R 1 = 48 / 100 =. 4800 R 0 = 60 / 100 =. 6000 RR 1 =. 48 /. 60 = 0. 80 Helicopter evacuation cut down on risk of death by 20% among serious accidents 5/20/2021 26

Minor Accidents Only (Stratum 2) Helicopter Road • • Died Survived Total 16 84 100 200 800 1000 R 1 = 16 / 100 =. 16 R 0 = 200 / 1000 =. 20 RR 2 =. 16 /. 20 = 0. 80 Helicopter evacuation cut down on risk of death by 20% among minor accidents 5/20/2021 27

Accident Evacuation Properties of Confounding • Seriousness of accident (Confounder) associated with method of evacuation (Exposure) • Seriousness of accident (Confounder) is an independent risk factor for death (Disease) • Seriousness of accident (Confounder) not in causal pathway between helicopter evaluation (Exposure) and death (Disease) 5/20/2021 28