Confounding and Effect Modification

1. Confounding and Effect Modification Karen Bandeen-Roche, Ph.D. Hurley-Dorrier Chair and Professor of Biostatistics July 20, 2010 JHU Intro to Clinical Research

2. Outline • Causal inference: comparing “otherwise similar” populations • “Confounding” is “confusing” • Graphical representation of causation • Addressing confounding • Effect modification JHU Intro to Clinical Research

3. Counterfactual Data Table JHU Intro to Clinical Research

4. Actual Data Table JHU Intro to Clinical Research

5. Goal of Statistical “Causal” Inference • “Fill-in” missing information in the counterfactual data table • Use data for persons receiving the other treatment to fill-in a persons missing outcome • Inherent assumption that the other persons are similar except for the treatment:“otherwise similar” • Compare like-to-like JHU Intro to Clinical Research

6. Confounding Confound means to “confuse” When the comparison is between groups that are otherwise not similar in ways that affect the outcome Simpson’s paradox; lurking variables,…. JHU Intro to Clinical Research

7. Confounding Example: Drowning and Ice Cream Consumption * * * * * * * Drowning rate * * * * * * * * * * * * * * * * * * * Ice Cream consumption JHU Intro to Clinical Research

8. Confounding Epidemiology definition: A characteristic “C” is a confounder if it is associated (related) with both the outcome (Y: drowning) and the risk factor (X: ice cream) and is not causally in between Ice Cream Consumption Drowning rate ?? JHU Intro to Clinical Research

9. Confounding Statistical definition: A characteristic “C” is a confounder if the strength of relationship between the outcome (Y: drowning) and the risk factor (X: ice cream) differs with, versus without, adjustment for C Ice Cream Consumption Drowning rate Outdoor Temperature JHU Intro to Clinical Research

10. Confounding Example: Drowning and Ice Cream Consumption * * * * * * * Drowning rate * * * * * * * * * Warm temperature * * * * * * * * * * Cool temperature Ice Cream consumption JHU Intro to Clinical Research

11. Mediation A characteristic “M” is a mediator if it is causally in the pathway by which the risk factor (X: ice cream) leads the outcome (Y: drowning) Ice Cream Consumption Drowning rate Cramping JHU Intro to Clinical Research

12. Confounding Statistical definition: A characteristic “C” is a confounder if the strength of relationship between the outcome and the risk factor differs with, versus without, comparing like to like on C Thought example: Outcome = remaining lifespan Risk factor = # diseases Confounder = age JHU Intro to Clinical Research

13. Example: Graduate School AdmissionsUC Berkeley JHU Intro to Clinical Research

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17. What is the lurking variable causing admissions rates to be lower in departments to which more women apply? Gender Admission ?? JHU Intro to Clinical Research

18. What is the lurking variable causing admissions rates to be lower in departments to which more women apply? Gender Admission Department to which applied JHU Intro to Clinical Research

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20. Controlling for Confounding Unadjusted (for department) difference in admission rates between women and men: 36-55 = -19% Adjusted (for department) difference in admission rates between women and men: Average(20, 5, -3, -4) = 4.5% Weighted ave(20, 5,-3, -4) = JHU Intro to Clinical Research

21. …Now for something entirely different Particulate air pollution and mortality JHU Intro to Clinical Research

22. Chicago July 2010

23. Los Angeles July 2010

24. Correlation: Daily mortality and PM10 • New York -0.031 • Chicago -0.036 • Los Angeles -0.019 Is season confounding the correlation between PM10 and mortality? What would happen if we “removed” season from the analysis? July 2010

25. Season-specific correlations July 2010

26. Overall association for New York July 2010

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31. Season-specific associations are positive, overall association is negative July 2010

32. Overall correlations July 2010

33. Overall correlations July 2010

34. Effect modification A characteristic “E” is an effect modifier if the strength of relationship between the outcome (Y: drowning) and the risk factor (X: ice cream) differs within levels of E Ice Cream Consumption Drowning rate Outdoor temperature JHU Intro to Clinical Research

35. Effect Modification Example: Drowning and Ice Cream Consumption * * * * * * * * * * Drowning rate * * * * * * Warm temperature * * * * * * * * * * Cool temperature Ice Cream consumption JHU Intro to Clinical Research

36. Question: Does aspirin use modify the association between treatment and adverse outcomes?

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40. Aspirin use modifies the effect of treatment on the risk of stroke? Losartan –vs- Atenolol Stroke Aspirin Use JHU Intro to Clinical Research

41. Summary • Causal inference: comparing “otherwise similar” populations • Confounding means confusing: comparing otherwise dissimilargroups • Stratify by confounders and make comparisons within strata, then pool results across strata to avoid the effects of confounding • Effect modification when the treatment effect varies by stratum of another variable JHU Intro to Clinical Research