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. Overview

14. Question: Do those with PVL+ MRSA die more than those that have PVL - MRSA? Example

15. 3154 Patients in MICU Example Flowchart

16. 3154 Patients in MICU 2648 Enrolled 506 Excluded Example Flowchart

17. 3154 Patients in MICU 2648 Enrolled 506 Excluded 1307 PVL+ MRSA 1341 PVL - MRSA Example Flowchart

18. 3154 Patients in MICU 2648 Enrolled 506 Excluded 1307 PVL+ MRSA 1341 PVL - MRSA 178 Deaths 1129 Living 79 Deaths 1262 Living Example Flowchart

19. What Type of Study is That? Example

20. What is the correct measure of association? Example

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

22. Data Do those with PVL+ MRSA die more than those that have PVL- MRSA? DeadAlive PVL+ MRSA PVL - MRSA Example

23. 3154 Patients in MICU 2648 Enrolled 506 Excluded 1307 PVL+ MRSA 1341 PVL - MRSA 178 Deaths 1129 Living 79 Deaths 1262 Living Example Flowchart

24. Do those with PVL+ MRSA die more than those that have PVL- MRSA? DeadAlive PVL+ MRSA1781129 PVL - MRSA 791262 Example Data

25. ? Well? Do those with PVL+ MRSA die more than those that have PVL- MRSA? Example

26. 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 those with PVL+ MRSA die more than those that have PVL- MRSA? Example

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

28. We noticed a lot of people with PVL+ MRSA also have COPD, much more than in patients with PVL- MRSA Could this be a confounder? Example

29. Input your data in the 2x2 Example: Step 1 DeadAlive PVL+ MRSA1781129 PVL - MRSA 791262 This gives you a ‘crude’ odds or risk ratio

30. Stratify on the potential confounder Stratified data: COPD + PVL + MRSA + Death: 168 PVL - MRSA + Death: 34 PVL + MRSA – Death: 880 PVL - MRSA - Death: 177 Stratified data: COPD - PVL + MRSA + Death: 10 PVL - MRSA + Death: 45 PVL + MRSA – Death: 249 PVL - MRSA - Death: 1085 Example: Step 2

31. Compute Risk Ratios for Both, Separately Example: Step 2 COPD-DeadAlive PVL+ MRSA PVL - MRSA COPD+DeadAlive PVL+ MRSA PVL - MRSA

32. Calculate the adjusted measure of association Stratified data: COPD + PVL + MRSA + Death: 168 PVL - MRSA + Death: 34 PVL + MRSA – Death: 880 PVL - MRSA - Death: 177 Stratified data: COPD - PVL + MRSA + Death: 10 PVL - MRSA + Death: 45 PVL + MRSA – Death: 249 PVL - MRSA - Death: 1085 Example: Step 2

33. 2. Compute Risk Ratios for Both, Separately Example: Step 2 COPD-DeadAlive PVL+ MRSA 10249 PVL - MRSA451085 COPD+DeadAlive PVL+ MRSA 168 880 PVL - MRSA 34 177

34. What do you see? Example

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

36. 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.

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

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

39. 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

40. 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

41. 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

42. 1: The way we just did it. This is probably the most reliable method with a few more steps. Multiple Confounding Variables

43. 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

44. 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.

45. 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?

46. 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!

47. 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

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

49. 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

50. 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

52. 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