1. Confounding

2. Objectives To define and discuss confounding To discuss methods of diagnosing confounding To define positive, negative and qualitative confounding To diagnose some data for confounding

3. 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).

4. 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. Example The association of alcohol related cirrhosis and TB mortality Which are predictors and confounders? –Age –Race –Gender –SES –Homelessness –Nutrition –HIV status –Access to care –Resistant strain of TB –Adherence to TB medication

8. Predictor, Confounder or Outcome Cell phone, auto accident, young age Vitamin A, diarrhea, childhood mortality Hepatitis C, alcohol, mortality Age, oral birth control, breast cancer incidence SES, race, cancer mortality Gender, SES, heart attack attributable mortality

9. Different strategies to assess confounding Examine crude and adjusted estimates of the association Stratification

12. 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