Avoiding Bias Due to Unmeasured Covariates

1. Avoiding Bias Due toUnmeasured Covariates 1. Introduction Alec Walker

2. Disclosure Alec Walker is a principal in World Health Information Science Consultants, LCC, a research and consulting firm whose clients include manufacturers or providers of health products and health-related services as well as public-private partnerships in the US. None of the material in this short course relates to a product in which Dr. Walker has a financial interest.

3. U T D

4. T D

5. Confounders U T D

6. Confounders U Randomization T D

7. Confounders U Randomization Self-matching T D

8. Confounders U Randomization Self-matching Proxies Proxies T D

9. Confounders U Randomization Self-matching Proxies Proxies T D Intermediates Intermediates

10. Confounders U Randomization Self-matching Proxies Proxies T D Intermediates Intermediates Instruments

11. Disarming Unmeasured Covariates

12. A “Classic” Example: Cimetidine and Gastric Cancer

13. Does cimetidine cause stomach cancer? • Case reports of de novo appearance in 1982 • Colin-Jones et al looked at data from ongoing work • Persons treated with cimetidine in a 12-month window • Matched to a comparison person • General practitioner • Age • Sex • Seen for another condition • Examined the incidence of stomach cancer

14. Diagnosed before cimetidine treatment started Diagnosed within six months of starting cimetidine treatment Diagnosed more than six months after starting cimetidine treatment Controls Cases of “early” cancer Diagnosed before cimetidine treatment started Diagnosed within six months of starting cimetidine treatment Diagnosed more than six months after starting cimetidine treatment Controls Cases of “early” cancer Number of cases Before study |  Study period  | After study Excess cases during follow-up Colin-Jones et al. Cimetidine and gastric cancer: preliminary report from post-marketing surveillance study. Brit Med J 1982;285:1311-1313

15. Hypotheses to account for excess cancers Colin-Jones and his coauthors suggested that • Stomach cancer incidence was only an artifact of treatment having come before diagnosis in disease was already present – The as-yet undetected disease caused the use of cimetidine and led to detected disease. They hypothesized that the effect would disappear with longer follow-up.

16. Excess persisted for years Colin-Jones DG. Postmarketing surveillance of the safety of cimetidine: mortality during second, third, and fourth years of follow up. Brit Med J 1985;291:1084-8

17. Hypotheses to account for excess deaths • Stomach cancer – The as-yet undetected disease caused the use of cimetidine and led to detected disease.

18. Hypotheses to account for excess deaths • Stomach cancer – The as-yet undetected disease caused the use of cimetidine and led to detected disease. • Lung cancer – Shared determinants. Cigarette smoking predisposes to persistence of stomach ulcer, which in turn leads to cimetidine use. The smoking also causes lung cancer.

19. Hypotheses to account for excess deaths • Stomach cancer – The as-yet undetected disease caused the use of cimetidine and led to detected disease. • Lung cancer – Shared determinants. Cigarette smoking predisposes to persistence of stomach ulcer, which in turn leads to cimetidine use. The smoking also causes lung cancer. • GI disease – Conditions that motivated cimetidine use led to death.

20. Hypotheses to account for excess deaths • Stomach cancer – The as-yet undetected disease caused the use of cimetidine and led to detected disease. • Lung cancer – Shared determinants. Cigarette smoking predisposes to persistence of stomach ulcer, which in turn leads to cimetidine use. The smoking also causes lung cancer. • GI disease – Conditions that motivated cimetidine use led to death. Under each of these hypotheses, cimetidine use was driven by unmeasured factors that also led to the outcomes. The argument was that cimetidine did not cause the deaths from GI disease, stomach cancer or lung cancer, but confounding created associations and the false appearance of causal relations.

21. But were any of these hypotheses correct? • Stomach cancer – The as-yet undetected disease caused the use of cimetidine and led to detected disease. • Lung cancer – Shared determinants. Cigarette smoking predisposes to persistence of stomach ulcer, which in turn leads to cimetidine use. The smoking also causes lung cancer. • GI disease – Conditions that motivated cimetidine use led to death. Colin-Jones and colleagues could not know for sure. Had they used a different study design, an answer might have been clear.

22. Randomization

23. Randomization • Treatment allocation is determined by a process • That generates An expectation of zero correlation between treatment and predictors of outcome. • The Predictors may be • Known or unknown to the experimenter • Measured or unmeasured • Measured poorly or well

24. Balance • All characteristics other than treatment are balanced in expectation • Measured and unmeasured • Predictors and correlates of predictors • The intermediate states that later arise from these

25. Balance • All characteristics other than treatment are balanced in expectation • Measured and unmeasured • Predictors and correlates of predictors • The intermediate states that later arise from these • Unadjusted estimates are unbiased estimates of treatment effect • Differences, ratios, more complex functions of • Risk, rates, hazards, survival, … • Costs, QoL, … • Even of dependent happenings, like epidemics (provided that exposure groups are not intermixed)

26. N Engl J Med. 2010 Apr 1;362(13):1192-202

27. Balance

28. Delta = Treatment Effect

29. Delta = Treatment Effect

30. Summary – Overview & Randomization • Anything that leads to both treatment and the outcome is a confounder. • Confounders distort estimates of treatment effect. • In observational studies there are many ways of blocking the effect of confounders, as long as the confounders are perfectly known. • When confounders are unmeasured or unknown, conventional methods for control do not apply. • Study design can break the association between unmeasured confounders and treatment or outcome. • Randomization of the assignment of treatment to individuals mechanically breaks the association between confounder and treatment.

31. Avoiding Bias Due toUnmeasured Covariates Presentations in this series Overview and Randomization Self-matching Proxies Intermediates Instruments Alec Walker