1. EPID 623-88 Introduction to Analysis and Interpretation of HIV/STD Data Confounding Manya Magnus, Ph.D. Summer 2001 adapted from M. O’Brien and P. Kissinger

2. Definition of Confounding A non-causal association between a given exposure and an outcome is observed as a result of the influence of a third variable (or group of variables) designated as confounding variable(s).

3. Rules of Confounding The confounding variable is: –Causally associated with the outcome –Non-causally or causally associated with the exposure –Not an intermediate variable in the causal pathway between exposure and outcome

5. Types of Positive – overestimation of the true strength of association Negative – underestimation of the true strength of association Qualitative – inverse in the direction of the association

8. Different strategies to assess confounding Examine crude and adjusted estimates of the association Stratification and examination of measures of association by strata

9. Crude Associations

13. More ideas about confounding Partial confounding can occur (not an all or nothing thing) Residual confounding (occurs when categories of confounders controlled for are too broad or when confounding variables remain unaccounted for)

16. Collinearity

18. Effect Modifiers

19. Interaction Two or more risk factors modify the effect of each other with regard to the occurrence or level of a given outcome Also known as effect modification Synergistic (positive interaction) – potentiates the effect of the exposure of interest Antagonistic (negative interaction) – diminishes or eliminates the effect of the exposure of interest

21. Confounding versus Interaction Sometimes the same variable may be both a confounder and an effect modifier Confounding makes it difficult to evaluate whether a statistical association is also causal Interaction is part of the web of causation Do not adjusted for a variable that is both a confounder and an effect modifer (reporting an average odds may be meaningless)

23. Risk factors for sinusitis among HIV-infected persons in Multivariate logistic regression