1. Tim Wiemken PhD MPH CIC Assistant Professor Division of Infectious Diseases University of Louisville, Kentucky Confounding

2. 1. Define and Identify Confounding 3. Identify How to Select Confounding Variables for Multivariate Analysis 2. Calculate Risk Ratio and Stratified Risk Ratio Overview

3. 1. Define and Identify Confounding 3. Identify How to Select Confounding Variables for Multivariate Analysis 2. Calculate Risk Ratio and Stratified Risk Ratio Overview

4. A variable related to the exposure (predictor) and outcome but not in the causal pathway Definition: Confounding

5. Confounding

6. Risk factor that has different prevalence in two study populations… e.g. Coffee drinking and lung cancer Why does this happen? Confounding

7. Men vs Women Example…. Men vs Women Example…. 25% Risk of lung cancer 5% Risk of Lung Cancer Example

8. Men vs Women Example…. Men vs Women Example…. 25% Risk of lung cancer 5% Risk of Lung Cancer Example Conclusion: People who drink coffee die more therefore coffee causes lung cancer

9. Men vs Women Example…. Men vs Women Example…. 25% Risk of lung cancer 5% Risk of Lung Cancer Example Truth: Coffee drinkers are more likely to smoke. Smoking is associated with a higher risk of lung cancer. mortality.

10. Example Outcome: Lung cancer Confounder: Smoking Predictor: Coffee

11. Example Outcome: Lung cancer Confounder: Smoking Predictor: Coffee Smoking associated with coffee drinking and lung cancer. Smoking is not caused by drinking coffee.

12. 1. Define and Identify Confounding 3. Identify How to Select Confounding Variables for Multivariate Analysis 3. Identify How to Select Confounding Variables for Multivariate Analysis 2. Calculate Risk Ratio and Stratified Risk Ratio 2. Calculate Risk Ratio and Stratified Risk Ratio Overview

13. Question: Are coffee drinkers more likely to get lung cancer? Example Warning: The upcoming data are made up. Do not make any decisions based on the outcomes of our example!

14. 3154 subjects 2648 Enrolled 506 Excluded 1307 coffee+ 1341 coffee- 178 cancer+ 1129 cancer- 79 cancer+ 1262 cancer- Example Flowchart

15. What Type of Study is That? Example

16. What is the correct measure of association? Example

17. What Type of Study is That? What is the correct measure of association? Example OK. Now Calculate the Correct Measure of Association

18. Data Do coffee drinkers get lung cancer more than non coffee drinkers? Cancer+Cancer- Coffee+ Coffee- Example

19. 3154 Subjects 2648 Enrolled 506 Excluded 1307 coffee+ 1341 coffee- 178 cancer+ 1129 cancer- 79 cancer+ 1262 cancer- Example Flowchart

20. Do coffee drinkers get lung cancer more than non coffee drinkers? Cancer+Cancer- Coffee+1781129 Coffee- 791262 Example Data

21. ? Well? Do coffee drinkers get lung cancer more than non coffee drinkers? Example

22. Yes! RR: 2.31, P=<0.001, 95% CI: 1.79 – 2.98 Yes! RR: 2.31, P=<0.001, 95% CI: 1.79 – 2.98 Do coffee drinkers get lung cancer more than non coffee drinkers? Example

23. Is this a true relationship or is another variable confounding that relationship? Example

24. We noticed a lot of coffee drinkers also smoke, much more than those patients who didn’t drink coffee. Could this be a confounder? Example

25. Input your data in the 2x2 Example: Step 1 Cancer+Cancer- Coffee+1781129 Coffee- 791262 This gives you a ‘crude’ odds or risk ratio

26. Stratify on the potential confounder Stratified data: Smoker+ Coffee+/ Cancer+: 168 Coffee -/Cancer+: 34 Coffee+/Cancer-: 880 Coffee-/Cancer-: 177 Stratified data: Smoker- Coffee+/ Cancer+: 10 Coffee -/Cancer+: 45 Coffee+/Cancer-: 249 Coffee-/Cancer-: 1085 Example: Step 2

27. Compute Risk Ratios for Both, Separately Example: Step 2 Smoker-Cancer+Cancer- Coffee+ Coffee- Smoker+Cancer+Cancer- Coffee+ Coffee-

28. Calculate the adjusted measure of association Example: Step 2 Stratified data: Smoker+ Coffee+/ Cancer+: 168 Coffee -/Cancer+: 34 Coffee+/Cancer-: 880 Coffee-/Cancer-: 177 Stratified data: Smoker- Coffee+/ Cancer+: 10 Coffee -/Cancer+: 45 Coffee+/Cancer-: 249 Coffee-/Cancer-: 1085

29. 2. Compute Risk Ratios for Both, Separately Example: Step 2 Smoker-Cancer+Cancer- Coffee+ 10249 Coffee-451085 Smoker+Cancer+Cancer- Coffee+ 168 880 Coffee- 34 177

30. What do you see? Example

31. Ensure that, in the group without the outcome, the potential confounder is associated with the predictor Example: Step 3

32. Adjusted Ratio Must be >10% Different than the Crude Ratio Example: Step 4 Compute the adjusted odds/risk ratios Compute the percent difference between the ‘crude’ and adjusted ratios.

33. If the criteria are met, you have a confounder Example

34. As in our example, a confounder can create an apparent association between the predictor and outcome. Issues with Confounding

35. As in our example, a confounder can create an apparent association between the predictor and outcome. A confounder can also mask an association, so it does not look like there is an association originally, but when you stratify, you see there is one. Issues with Confounding

36. 1. Define and Identify Confounding 3. 3. Identify How to Select Confounding Variables for Multivariate Analysis 3. 3. Identify How to Select Confounding Variables for Multivariate Analysis 2. Calculate Risk Ratio and Stratified Risk Ratio 2. Calculate Risk Ratio and Stratified Risk Ratio Overview

37. Regression methods adjust for multiple confounding variables at once – less time consuming. Logistic Regression Linear Regression Cox Proportional Hazards Regression … and many others Logistic Regression Linear Regression Cox Proportional Hazards Regression … and many others Multiple Confounding Variables

38. 1: The way we just did it. This is probably the most reliable method with a few more steps. Multiple Confounding Variables

39. 2. Include all clinically significant variables or those that are previously identified as confounders. Issues: May have too many confounders Confounding in other studies does NOT mean it is a confounder in yours. Issues: May have too many confounders Confounding in other studies does NOT mean it is a confounder in yours. Multiple Confounding Variables

40. 3: If that variable is significantly associated with the outcome (chi- squared) then include it. Multiple Confounding Variables Sun, G. W., Shook, T. L., & Kay, G. L. (1996). Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol, 49(8), 907-916.

41. 3: If that variable is significantly associated with the outcome (chi- squared) then include it. Many issues with this method. Multiple Confounding Variables Sun, G. W., Shook, T. L., & Kay, G. L. (1996). Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol, 49(8), 907-916. What is significant?

42. 3: If that variable is significantly associated with the outcome (chi- squared) then include it. Many issues with this method. Multiple Confounding Variables Sun, G. W., Shook, T. L., & Kay, G. L. (1996). Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol, 49(8), 907-916. Just because the ‘confounder’ is associated with the predictor doesn’t mean it is associated with the outcome and not in the causal pathway!

43. 4. Automatic Selection Regression Methods Many ways to do this, and relatively reliable with certain methods. Forward Selection Backward Selection Stepwise Many ways to do this, and relatively reliable with certain methods. Forward Selection Backward Selection Stepwise Multiple Confounding Variables

44. Caveats Need to control for as few confounding variables as possible. Multiple Confounding Variables

45. Caveats Need to control for as few confounding variables as possible. You are limited by the number of cases of the outcome you have (10:1 Rule) Multiple Confounding Variables

46. Caveats Need to control for as few confounding variables as possible. You are limited by the number of cases of the outcome you have (10:1 Rule) Some journals just want it done a certain way. Multiple Confounding Variables

48. 1. Define and Identify Confounding 3. 3. Identify How to Select Confounding Variables for Multivariate Analysis 3. 3. Identify How to Select Confounding Variables for Multivariate Analysis 2. Calculate Risk Ratio and Stratified Risk Ratio 2. Calculate Risk Ratio and Stratified Risk Ratio Overview