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

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

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

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

Confounding

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

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

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

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.

Example Outcome: Lung cancer Confounder: Smoking Predictor: Coffee

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

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

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!

3154 subjects 2648 Enrolled 506 Excluded 1307 coffee coffee- 178 cancer cancer- 79 cancer cancer- Example Flowchart

What Type of Study is That? Example

What is the correct measure of association? Example

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

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

3154 Subjects 2648 Enrolled 506 Excluded 1307 coffee coffee- 178 cancer cancer- 79 cancer cancer- Example Flowchart

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

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

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

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

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

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

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

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

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

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

What do you see? Example

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

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.

If the criteria are met, you have a confounder Example

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

1. Define and Identify Confounding Identify How to Select Confounding Variables for Multivariate Analysis 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

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

1: The way we just did it. This is probably the most reliable method with a few more steps. 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. Issues: May have too many confounders Confounding in other studies does NOT mean it is a confounder in yours. Multiple Confounding Variables

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),

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), What is significant?

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), Just because the ‘confounder’ is associated with the predictor doesn’t mean it is associated with the outcome and not in the causal pathway!

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

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

1. Define and Identify Confounding Identify How to Select Confounding Variables for Multivariate Analysis 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