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Tim Wiemken PhD MPH CIC Assistant Professor Division of Infectious Diseases

# Tim Wiemken PhD MPH CIC Assistant Professor Division of Infectious Diseases

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## Tim Wiemken PhD MPH CIC Assistant Professor Division of Infectious Diseases

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

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

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

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

5. Confounding

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

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

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

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

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

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

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

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

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

15. Example What Type of Study is That?

16. Example What Type of Study is That? What is the correct measure of association?

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

18. Example Do coffee drinkers get lung cancer more than non coffee drinkers? Data

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

20. Example Do coffee drinkers get lung cancer more than non coffee drinkers? Data

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

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

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

24. Example Is this a true relationship or is another variable confounding that relationship? We noticed a lot of coffee drinkers also smoke, much more than those patients who didn’t drink coffee. Could this be a confounder?

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

26. Example: Step 2 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

27. Example: Step 2 Compute Risk Ratios for Both, Separately

28. Example: Step 2 Calculate the adjusted measure of association 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. Example: Step 2 2. Compute Risk Ratios for Both, Separately

30. Example What do you see?

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

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

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

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

35. Issues with Confounding 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.

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

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

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

39. Multiple Confounding Variables 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.

40. Multiple Confounding Variables 3: If that variable is significantly associated with the outcome (chi-squared) then include it. 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. Multiple Confounding Variables 3: If that variable is significantly associated with the outcome (chi-squared) then include it. Many issues with this method. What is significant? 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.

42. Multiple Confounding Variables 3: If that variable is significantly associated with the outcome (chi-squared) then include it. Many issues with this method. Just because the ‘confounder’ is associated with the predictor doesn’t mean it is associated with the outcome and not in the causal pathway! 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.

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

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

45. Multiple Confounding Variables 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)

46. Multiple Confounding Variables 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.

47. Multiple Confounding Variables

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