Epidemiology Kept Simple

1. Epidemiology Kept Simple Chapter 12 Error in Epidemiologic Research

2. §12.1 Introduction • View analytic studies as exercises in measuring associationbetween 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

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

4. Target Analogy Let Φrepresent 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. Target Analogy

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

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

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

9. Hypothesis Tests • Start with this null hypothesis H0: RR = 1 (no association) • Use data to calculate a test statistic • Use the test statistic to derive a P-value • Definition:P-value ≡ probability of the data, or data more extreme, assuming H0is true • InterpretP-value as a measure of evidence against H0 (small P gives one reason to doubt H0) R.A. Fisher

10. Conventions for Interpretation • Small P-value provide strong evidence against H0 (with respect to random error explanations only!) • By convention • P ≤ .10 is considered marginally significant evidence against H0 • P≤ .01 is considered highly significant evidence against H0 • Do NOT use arbitrary cutoffs (“surely, god loves P = .06”) R.A. Fisher

11. Childhood SES and Stroke Nonsignif. evidence against H0 Marginally significant evidence against H0 Source: Galobardes et al., Epidemiologic Reviews, 2004, p. 14

12. 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 (over-estimates risks or benefits)

13. 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

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

15. Examples of Information Bias • Recall bias • Diagnostic suspicion bias • Obsequiousness bias • Clever Hans effect • Read about it on page 231

16. Memory Bias • Memory is constructed rather than played back • Example of a memory bias is The Misinformation Effect≡ a 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 Loftus, E. F. & Palmer, J. C. (1974). Reconstruction of automobile destruction. Journal of Verbal Learning and Verbal Behaviour, 13, 585-589.

17. Recall Bias in Case-Control Studies

18. Differential & Nondifferential Misclassification • Non-differential misclassification: groups equally misclassified • Differential misclassification: groups misclassified unequally

19. Nondifferential and Differential Misclassification Illustrations

20. Confounding • Adistortion 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

21. Properties of a Confounding Variable • Associated with the exposure • An independent risk factor • Not in causal pathway

22. Example of Confounding E ≡ Evacuation method D ≡ Death following auto accident • R1 = 64 / 200 = .3200 • R0 = 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?

23. Serious Accidents Only (Stratum 1) • R1 = 48 / 100 = .4800 • R0 = 60 / 100 = .6000 • RR1 = .48 / .60 = 0.80 • Helicopter evacuation cut down on risk of death by 20% among serious accidents

24. Minor Accidents Only (Stratum 2) • R1 = 16 / 100 = .16 • R0 = 200 / 1000 = .20 • RR2 =.16 / .20 = 0.80 • Helicopter evacuation cut down on risk of death by 20% among minor accidents

25. Accident EvacuationProperties 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)