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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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**Confounding**Tim Wiemken PhD MPH CIC Assistant Professor Division of Infectious Diseases University of Louisville, Kentucky**Overview**1. Define and Identify Confounding 2. Calculate Risk Ratio and Stratified Risk Ratio 3. Identify How to Select Confounding Variables for Multivariate Analysis**Overview**1. Define and Identify Confounding 2. Calculate Risk Ratio and Stratified Risk Ratio 3. Identify How to Select Confounding Variables for Multivariate Analysis**Confounding**Definition: A variable related to the exposure (predictor) and outcome but not in the causal pathway**Confounding**Why does this happen? Risk factor that has different prevalence in two study populations… e.g. Coffee drinking and lung cancer**Example**Men vs Women Example…. 25% Risk of lung cancer 5% Risk of Lung Cancer**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**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.**Example**Predictor: Coffee Outcome: Lung cancer Confounder: Smoking**Example**Predictor: Coffee Outcome: Lung cancer Confounder: Smoking Smoking associated with coffee drinking and lung cancer. Smoking is not caused by drinking coffee.**Overview**1. Define and Identify Confounding 2. Calculate Risk Ratio and Stratified Risk Ratio 3. Identify How to Select Confounding Variables for Multivariate Analysis**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!**Example Flowchart**178 cancer+ 1307 coffee+ 1129 cancer- 2648 Enrolled 79 cancer+ 1341 coffee- 3154 subjects 1262 cancer- 506 Excluded**Example**What Type of Study is That?**Example**What Type of Study is That? What is the correct measure of association?**Example**What Type of Study is That? What is the correct measure of association? OK. Now Calculate the Correct Measure of Association**Example**Do coffee drinkers get lung cancer more than non coffee drinkers? Data**Example Flowchart**178 cancer+ 1307 coffee+ 1129 cancer- 2648 Enrolled 79 cancer+ 1341 coffee- 3154 Subjects 1262 cancer- 506 Excluded**Example**Do coffee drinkers get lung cancer more than non coffee drinkers? Data**Example**Do coffee drinkers get lung cancer more than non coffee drinkers? Well?**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**Example**Is this a true relationship or is another variable confounding that relationship?**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?**Example: Step 1**Input your data in the 2x2 This gives you a ‘crude’ odds or risk ratio**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**Example: Step 2**Compute Risk Ratios for Both, Separately**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**Example: Step 2**2. Compute Risk Ratios for Both, Separately**Example**What do you see?**Example: Step 3**Ensure that, in the group without the outcome, the potential confounder is associated with the predictor**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**Example**If the criteria are met, you have a confounder**Issues with Confounding**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.**Overview**1. Define and Identify Confounding 2. Calculate Risk Ratio and Stratified Risk Ratio 3. Identify How to Select Confounding Variables for Multivariate Analysis**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**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.**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.**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.**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.**Multiple Confounding Variables**4. Automatic Selection Regression Methods • 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.**Overview**1. Define and Identify Confounding 2. Calculate Risk Ratio and Stratified Risk Ratio 3. Identify How to Select Confounding Variables for Multivariate Analysis