Designing Clinical Research 2009

1. Designing Clinical Research 2009 Confounding and Causal Inference Warren Browner

2. Confounding • Guaranteed to be confusing. • If you think you understand it, you probably don’t. • I often revert to an analogy involving smoking, carrying matches, and lung cancer.

3. Confounding, Round One • A problem in understanding what the association between two variables means. • No association, no confounding to worry about Example: A study which finds that people who carry matches are more likely to get lung cancer.

4. Confounding, Round Two • Confounding only matters in studies that attempt to show that an association between a predictor and an outcome is causal. • Is the association between carrying matches and lung cancer causal? • Or is the association confounded by something else?

5. Confounding, Round Three • Confounding is irrelevant in… • Diagnostic and prognostic test studies • Is cough a useful sign for the diagnosis of lung cancer? • Risk assessment • You’re trying to determine whether people who carry matches are at greater risk of lung cancer.

6. Confounding, A Picture Lung cancer (outcome) Carrying matches (predictor?)

7. Confounding, A Picture Smoking cigarettes (confounder) Lung cancer (outcome) Carrying matches (predictor?)

8. Confounding, A Picture Smoking cigarettes (confounder) Lung cancer (outcome) Carrying matches (predictor?) Carrying matches does not cause lung cancer; smoking does.

9. Huh, Aren’t All Associations Causal? • Nope. Most aren’t! • Hard for us to believe. We excel at ascribing causes. • Think about the last time you “just knew” someone who wronged you did so intentionally.

10. What are Some Other Explanations? • The Big Five • Chance • Bias • Effect-cause • Confounding • Cause-effect

11. Five Potential Explanations for Associations Made in Observational Studies Spurious (not real) 1) Chance Bottled water and lung cancer (tested 100 exposures, P < 0.05) 2) Bias Coffee and lung cancer (controls had GERD)

12. Five Potential Explanations for Associations Made in Observational Studies Real, but not causal 3) Effect – cause Cough and lung cancer • Confounding Carrying matches and lung cancer

13. Five Potential Explanations for Associations Made in Observational Studies Causal 5) Cause – effect Cigarettes and lung cancer

14. Surely This is a Mistake that Pros Don’t Make • They do. • Famous “proof” that smoking causes cervical cancer (i.e., that confounding was unlikely). • But confounding can result in strong associations.

15. An Example of Confounding CA NO CA MATCHES NO MATCHES OR = ??? Case-control study: 1000 cases of lung cancer, 82% carry matches (or a lighter) 1000 controls (no cancer), 34% carry matches

16. An Example of Confounding CA NO CA 820 340 MATCHES 180 660 NO MATCHES 820x660 180x340 OR = = 8.84 Carrying matches increases the risk (odds) of lung cancer almost 9-fold!

17. What’s the Problem? • It is ABSOLUTELY true that people who carry matches are at increased risk of lung cancer • But it is NOT true (right?) that carrying matches causes lung cancer. • What is going on? (Duh…)

18. What’s Going On? • 90% of lung cancer patients smoke • 30% of controls smoke • 90% of smokers carry matches • 10% of non-smokers carry matches • Smoking confounds the association between carrying matches and lung cancer

19. I mean, the odds ratio was almost 9! Still Skeptical?

20. Let’s Work This Out • 1000 cases, 820 carry matches • 900 smoke, 90% of whom carry matches • 100 don’t smoke, 10% of whom carry matches • 1000 controls, 340 carry matches • 300 smoke, 90% of whom carry matches • 700 don’t smoke, 10% of whom carry matches

21. All You Need to Know • Stratify by Exposure to the Confounder (SEC) • In this case, smoking is the confounder (“Smoking confounds the association between carrying matches and lung cancer.”) • The analyses should be stratified by smoking. • Look separately among smokers and non-smokers

22. Stratify by Exposure to Confounder (SEC) SMOKERS NON-SMOKERS CA NO CA CA NO CA MATCHES MATCHES NO MATCHES NO MATCHES ORnon-smokers = ?? ORsmokers = ?? • 1000 cases, 820 carry matches • 900 smoke, 90% of whom carry matches • 100 don’t smoke, 10% of whom carry matches • 1000 controls, 340 carry matches • 300 smoke, 90% of whom carry matches • 700 don’t smoke, 10% of whom carry matches

23. Stratify by Exposure to Confounder (SEC) SMOKERS NON-SMOKERS CA NO CA CA NO CA 10 810 MATCHES MATCHES NO MATCHES 90 90 NO MATCHES ORnon-smokers = ?? ORsmokers = ?? • 1000 cases, 820 carry matches • 900 smoke, 90% of whom carry matches • 100 don’t smoke, 10% of whom carry matches • 1000 controls, 340 carry matches • 300 smoke, 90% of whom carry matches • 700 don’t smoke, 10% of whom carry matches

24. 810x30 90x270 Stratify by Exposure to Confounder (SEC) SMOKERS NON-SMOKERS CA NO CA CA NO CA 10 70 810 270 MATCHES MATCHES NO MATCHES 90 630 90 30 NO MATCHES 10x630 90x70 ORnon-smokers = = 1.0 ORsmokers = = 1.0

25. After Stratifying by Smoking… • There is no association between carrying matches and lung cancer in smokers (OR = 1). • There is no association between carrying matches and lung cancer in non-smokers (OR = 1). • Thus the association between carrying matches and lung cancer is completely confounded by smoking.

26. Confounding and Confounders • Confounding occurs when the association between two variables (a predictor and an outcome) is due to a third variable (the confounder). • The confounder must also be causally related to the outcome. • Subtle point, but true

27. Subtle Digression • One would NOT say that the association between carrying matches and lung cancer was confounded by having ashtrays in the home. • Though the same kind of arithmetic might hold.

28. My Rules • Never assume “it can’t be confounding” in an observational study. • Be humble and skeptical.

29. Dealing with Confounding Does carrying matches cause lung cancer? Design phase strategies Specification Matching Analysis phase strategies Stratification Adjustment

30. Specification and Matching • Both terms refer to the confounder • Specify: only study those with (or without) the confounder • Look at carrying matches and lung cancer only in non-smokers • Match: make sure that a case who does (not) smoke is matched to a control who does (not). • Then compare the frequency of carrying matches

31. Stratification and Adjustment • Again, both terms refer to the confounder • Stratify the analyses by exposure to the confounder • Adjust the analyses for exposure to the confounder

32. It’s Not Just Ruling Things Out • Even if you eliminate (or make unlikely) chance, bias, confounding, and effect-cause, it’s nice to have “positive evidence” of causality

33. “Positive” Evidence of Causality Temporality Consistency Dose-response Plausibility

34. I Misled You Before • There can be confounding when there’s no association. • If a confounder weakens the association between the predictor and the outcome. • Sometimes called suppression or reverse confounding. • Don’t lose sleep about this for all (and often any) of your null findings.

35. Interaction and Effect Modification • Almost nobody understands these (they’re the same thing!) • But what it means is not that complicated. • The effect of one predictor on an outcome varies by the presence of another predictor.

36. Interaction, Example from the 70’s • The effect of social class on survival depended on country. • In Viet Nam, higher social class was associated with better survival. • In Cambodia, higher social class was associated with worse survival.

37. Interaction, Example from the 70’s • The effect of country on survival depended on social class. • Among the rich, being in Viet Nam was associated with better survival. • Among the poor, being in Cambodia was associated with better survival.

38. That’s the End… Confounded?