Bias can get by us

1. Bias can get by us November 2 2004 Epidemiology 511 W. A. Kukull

2. Bias • Systematic error that leads to incorrect estimate of an association • anticipate and eliminate or minimize in the study design phase • may be impossible to account for in analysis • usually introduced by the investigator (or subjects) • Main categories: Selection bias and Information bias

3. Bias is a systematic error(diagram after Rothman, 2002) • Random error decreases with study size; systematic error remains Random error Error Systematic error Study size

4. Direction of Bias

5. Control of Bias • Careful study design is primary • Selection bias: permanent flaw • Choice of study groups • Data Collection; Data sources • objective, closed ended questions • trained interviewers: reliability assessment • wide variety of factors to “blind” interviewer and subject to hypothesis

6. Selection Bias • Selection of “cases” or “controls” leads to apparent disease- exposure association • Selection or f/u and dx of “exposed” or “unexposed” leads to apparent d - e association • “Apparent” association is due to a systematic error in design or conduct of the study

7. Selection bias • Common element: • The association between exposure and disease is different for those who are studied than it is for those who would be eligible but are not studied • Case - control: subject selection is influenced by probability of exposure history • Cohort: non-random loss to follow-up influences association measure (RR)

8. “Population” base Framinghammer City Non- diseased Loss, death, refusals before disease develops Study enrollees Disease cases Time

9. Selection Bias Reference Population Dis No Dis Exp Study Sample Not Exp Non-Reference probabilities of being included in the study within exposure (or disease)

10. Example: selection bias(after Szklo & Neito, 2000) True reference population disease No disease Exp 1800 500 Not Exp 500 7200 OR = 4.0

11. Unbiased Sample re: exposure status 50% of Diseased; 10% of Not Diseased-- but true Reference proportions of “exposed” in each Not D D 180 Exp 250 250 720 Not Exp OR= 4.0

12. Biased exposure probability sampling among “diseased” ONLY ( 60% exposed, not true 50% ) due to a flawed design or strategy Dis Not Dis Exp 300 180 Not Exp 200 720 OR = 6.0

13. Basic example: Case-control study(after Hernan et al, 2004) • Is prior HRT use associated with MI? • Select women with incident MI—cases • Select controls from women with high frequency of hip fracture (unintentionally) • HRT is known to decrease osteoporosis • Is the HRT – MI association likely to be biased ? Why/how?

14. Hospital-base case-control study:Berkson’s bias (after Schwartzbaum et al,2003) • Premise: diseases have different probabilities hospital admission • Pr(brain injury) > Pr(allergic rhinitis) • Pr( >2 diseases) > Pr( 1 disease) • Diseases unassociated in the population could be associated in hospitalized patients • Then, a risk factor for one disease could appear to be a risk factor for the other

15. Berkson’s bias/Admission bias(after Sackett, 1979) Bone disease Yes No Resp. Disease Yes No 5 15 17 207 Yes 18 219 184 2376 No Hospitalized in Last 6 months OR=4.06 Gen. Pop. OR=1.06

16. Loss to follow-up: Selection bias in a Cohort study • Effects of anti-retroviral therapy hx on AIDS risk in HIV+ patients. • Pts. with more symptoms may drop early • Pts. with more therapy side effects may drop • Restricting analysis to non-drop outs can produce biased result • Subject drop out is rarely “at random” • Statistical missing data strategies

17. Selection Biases • Non-response/Missing data bias: characteristics may differ between early, late and nonresponders • Missing data proportions differ • Analyses restricted to complete data will be biased • Non-responders in case-control studies may have different exposure histories

18. Healthy Worker selection bias • Do rubber industry workers have excess mortality compared with U.S. population of the same age and sex? • SMR = 82 for rubber workers • General population includes people who are unable to work because of illness • All cause death rates are usually higher in the general pop. than among workers • Use unexposed workers as a comparison group

19. Contributors to selection bias • Choice of comparison group or sampling frame • Self-selection, volunteers • Loss to follow-up (cohort) • Initial non-response • primarily case-control studies • Selective survival • Differences in disease detection (surveillance or detection bias)

20. Examples • Unmasking bias: • physicians followed OC users more closely because of use-related cautions and thus detected more thrombophlebitis • Frequent visits =>more comorbidity • Prevalent case and Survival bias • Smoking and Alzheimer’s disease • Among AD cases smokers may have shorter survival than non-smokers

21. Prevalent case biasLonger disease duration increases chance of selection Cross-sectional Sample Time

22. Example: volunteer/self-selection • Leukemia in troops present at atomic test site • 76% of all troops were traced • of the 76%, 82% were tracked down by investigators • of the 76%, 18% contacted investigators on their own initiative • 4 leukemia cases were among the 18% and 4 among the 82%--Self referral bias?

23. Information Bias • Inadequacies and inaccuracies in data collection or measurement • Common to all subjects? • Will reduce observed association • Different in each comparison group? • may exaggerate association

24. Information Bias • Systematic errors in obtaining needed exposure (or diagnosis) information • non-differential misclassification, “random” error • usually biases toward the “null” • differential misclassification: different between the study groups • may cause estimated effect error in either direction

25. Example:True classification of family history for a hypothetical disease ‘X’ Disease X No Disease Positive Family Hx 240 80 No Family Hx 320 160 400 400 OR= 6.0

26. Example:Non-Differential misclassificationFam Hx accuracy cases 65%; controls 65% Disease X No X Family Hx 52 156 No Fam Hx 244 348 OR = 4.3 400 400

27. Example: Differential misclassificationaccuracy cases 85%; controls 25% Disease X No X Family Hx 204 20 No Family Hx 196 380 OR = 19.8 400 400

28. Cohort study: true classification of persons who hypothetically develop ER(after Koepsell & Weiss, Chapt 10)

29. What if only 90% of the true cases were identified due to diagnostic inaccuracy?

30. What if 1.0% of the well persons were misdiagnosed as having ER, but didn’t

31. Information Bias • Example: MI and smoking • smokers with new MI may be less likely to respond to a mailed questionnaire than non-smokers with new MI • if the non response is related to exposure and disease the potential for bias exists • Proxy reports of exposure • Relationship, proximity influence agreement

32. Information Biases(after Sackett) • Diagnostic suspicion bias: knowledge of subjects prior history influences intensity of diagnostic effort • Exposure suspicion bias: disease with “known” cause may increase search for that cause

33. Information Biases(after Sackett) • Recall bias: cases more (or less) likely to report than controls • Family information bias: Information from a family is stimulated by a new case in in the family--and their need to explain why

34. Background random factors (chance) Correlated causes, confounding Diagnostic inaccuracy Exposure accuracy Missing data, database errors Group/hypothesis formation Case-control selection Cohort loss to f/u Analysis, modeling, interpretation Publication bias Editors and experts Exposure Diseaseviewed through(after Maclure & Schneeweiss, 2001)

35. Evaluation of Bias:What would the RR look like if ??? • What is the direction and likely effect if bias is active? • IS A TRUE ASSOCIATION MASKED? • IS A SPURIOUS ASSOCIATION REPORTED? • Can the potential for recall bias be estimated • second control group with another illness?

36. Is Selection Bias Present(after Grimes and Shultz, Lancet;2002;359:248-52) • In a cohort study, are participants in the exposed and unexposed groups similar in all respects except for exposure? • In a case control study, are cases and controls similar in important respects except for the disease in question?

37. Is Information Bias Present(after Grimes and Shultz, Lancet;2002;359:248-52) • In a cohort study, is information about outcome obtained in the same way for those exposed and unexposed? • In a case control study, information about exposure gathered in the same way for cases and controls?

38. Is Confounding Present(after Grimes and Shultz, Lancet;2002;359:248-52) • Could the results be accounted for by the presence of another factor– e.g., age, smoking, sexual behavior, diet—associated with the exposure and outcome but not directly in the causal pathway? • Confounding is the subject of another lecture…

39. If Not bias or confounding are results due to “chance”(after Grimes and Shultz, Lancet;2002,359:248-52) • What is the RR or OR and the 95% confidence intervals…Does the CI include 1.0? • Is the difference (association) statistically significant and if not did the study have adequate power to find a clinically important difference (association)? • What is the p-value? • Is the p-value inflated by multiple comparisons ?

40. Bias and study designs:Important sources • Case-control • Knowledge of disease status may influence determination of exposure status • Knowledge of exposure status influenced the subjects selected • Recall bias • Cohort • loss to follow-up; differential misdiagnosis • Information bias

41. Epidemiologic Reasoning • Use the tools, statistics and calculations • Use knowledge of biology, behavior and disease pathogenesis • Make educated guesses about effect of bias and confounding to guide study design and analysis and eliminate untoward effects • Try to make causal inferences

42. Conclusion • What sources of Bias are common to which study designs? • How can we evaluate bias? • “Sensitivity analysis”: “What if….” • Confounding may still impact results even if bias is eliminated—but it can be dealt with in analysis.