Tim Wiemken PhD MPH CIC Assistant Professor Division of Infectious Diseases
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Transcript of Tim Wiemken PhD MPH CIC Assistant Professor Division of Infectious Diseases
Tim Wiemken PhD MPH CICAssistant Professor
Division of Infectious Diseases University of Louisville, Kentucky
ConfoundingConfounding
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:
ConfoundingConfounding
ConfoundingConfounding
Risk factor that has different prevalence intwo study populations…
e.g. Coffee drinking and lung cancer
Why does this happen?
ConfoundingConfounding
Men vs Women Example….Men vs Women Example….
25% Risk of lung cancer
5% Risk of Lung Cancer
ExampleExample
Men vs Women Example….Men vs Women Example….
25% Risk of lung cancer
5% Risk of Lung Cancer
ExampleExample
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
ExampleExample
Truth: Coffee drinkers are more likely to smoke. Smoking is associated with a higher risk of lung cancer.
mortality.
ExampleExample
Outcome: Outcome: Lung cancerLung cancer
Confounder: Confounder: SmokingSmoking
Predictor: Predictor: CoffeeCoffee
ExampleExample
Outcome: Outcome: Lung cancerLung cancer
Confounder: Confounder: SmokingSmoking
Predictor: Predictor: CoffeeCoffee
Smoking associated with coffee drinking and lung cancer. Smoking is not caused by drinking coffee.
1. Define and Identify Confounding 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
OverviewOverview
Question: Are coffee drinkers more likely to get lung cancer?
ExampleExample
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+
1341 coffee-
178 cancer+
1129 cancer-
79 cancer+
1262 cancer-
Example FlowchartExample Flowchart
What Type of Study is That?
ExampleExample
What Type of Study is That?
What is the correct measure of association?
ExampleExample
What Type of Study is That?
What is the correct measure of association?
ExampleExample
OK. Now Calculate the Correct Measure of Association
Data
Do coffee drinkers get lung cancer more than non coffee drinkers?
Cancer+ Cancer-
Coffee+
Coffee-
ExampleExample
3154 Subjects
2648 Enrolled
506 Excluded
1307 coffee+
1341 coffee-
178 cancer+
1129 cancer-
79 cancer+
1262 cancer-
Example FlowchartExample Flowchart
Do coffee drinkers get lung cancer more than non coffee drinkers?
Cancer+ Cancer-
Coffee+ 178 1129
Coffee- 79 1262
ExampleExample
Data
Well??
Do coffee drinkers get lung cancer more than non coffee drinkers?
ExampleExample
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?
ExampleExample
Is this a true relationship or is another variable confounding that relationship?
ExampleExample
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?
ExampleExample
Input your data in the 2x2
Example: Step 1Example: Step 1
Cancer+ Cancer-
Coffee+ 178 1129
Coffee- 79 1262
This gives you a ‘crude’ odds or risk ratio
Stratify on the potential confounder
Stratified data:Smoker+
Coffee+/ Cancer+: 168Coffee -/Cancer+: 34Coffee+/Cancer-: 880Coffee-/Cancer-: 177
Stratified data:Smoker-
Coffee+/ Cancer+: 10Coffee -/Cancer+: 45Coffee+/Cancer-: 249Coffee-/Cancer-: 1085
Example: Step 2Example: Step 2
Compute Risk Ratios for Both, Separately
Example: Step 2Example: Step 2
Smoker- Cancer+ Cancer-
Coffee+
Coffee-
Smoker+ Cancer+ Cancer-
Coffee+
Coffee-
Calculate the adjusted measure of association
Example: Step 2Example: Step 2
Stratified data:Smoker+
Coffee+/ Cancer+: 168Coffee -/Cancer+: 34Coffee+/Cancer-: 880Coffee-/Cancer-: 177
Stratified data:Smoker-
Coffee+/ Cancer+: 10Coffee -/Cancer+: 45Coffee+/Cancer-: 249Coffee-/Cancer-: 1085
2. Compute Risk Ratios for Both, Separately
Example: Step 2Example: Step 2
Smoker- Cancer+ Cancer-
Coffee+ 10 249
Coffee- 45 1085
Smoker+ Cancer+ Cancer-
Coffee+ 168 880
Coffee- 34 177
What do you see?
ExampleExample
Ensure that, in the group without the outcome, the potential confounder is associated with
the predictor
Example: Step 3Example: Step 3
Adjusted Ratio Must be >10% Different than the Crude Ratio
Adjusted Ratio Must be >10% Different than the Crude Ratio
Example: Step 4Example: Step 4
Compute the adjusted odds/risk ratiosCompute the adjusted odds/risk ratios
Compute the percent difference between the ‘crude’ and adjusted ratios.
Compute the percent difference between the ‘crude’ and adjusted ratios.
If the criteria are met, you have a confounder
If the criteria are met, you have a confounder
ExampleExample
As in our example, a confounder can create an apparent association between
the predictor and outcome.
As in our example, a confounder can create an apparent association between
the predictor and outcome.
Issues with ConfoundingIssues with Confounding
As in our example, a confounder can create an apparent association between
the predictor and outcome.
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.
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 ConfoundingIssues with Confounding
1. Define and Identify Confounding 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
OverviewOverview
Regression methods adjust for multiple confounding variables at once
– less time consuming.
Regression methods adjust for multiple confounding variables at once
– less time consuming.
Logistic RegressionLinear Regression
Cox Proportional Hazards Regression… and many others
Logistic RegressionLinear Regression
Cox Proportional Hazards Regression… and many others
Multiple Confounding VariablesMultiple Confounding Variables
1: The way we just did it. 1: The way we just did it.
This is probably the most reliable method with a few more steps.
This is probably the most reliable method with a few more steps.
Multiple Confounding VariablesMultiple Confounding Variables
2. Include all clinically significant variables or those that are previously
identified as confounders.
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 VariablesMultiple Confounding Variables
3: If that variable is significantly associated with the outcome (chi-
squared) then include it.
3: If that variable is significantly associated with the outcome (chi-
squared) then include it.
Multiple Confounding VariablesMultiple 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.
3: If that variable is significantly associated with the outcome (chi-
squared) then include it.
3: If that variable is significantly associated with the outcome (chi-
squared) then include it.
Many issues with this method.Many issues with this method.
Multiple Confounding VariablesMultiple 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?What is significant?
3: If that variable is significantly associated with the outcome (chi-
squared) then include it.
3: If that variable is significantly associated with the outcome (chi-
squared) then include it.
Many issues with this method.Many issues with this method.
Multiple Confounding VariablesMultiple 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!
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
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 VariablesMultiple Confounding Variables
Caveats Caveats
Need to control for as few confounding variables as possible.
Need to control for as few confounding variables as possible.
Multiple Confounding VariablesMultiple Confounding Variables
Caveats Caveats
Need to control for as few confounding variables as possible.
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)
You are limited by the number of cases of the outcome you have (10:1 Rule)
Multiple Confounding VariablesMultiple Confounding Variables
Caveats Caveats
Need to control for as few confounding variables as possible.
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)
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.
Some journals just want it done a certain way.
Multiple Confounding VariablesMultiple Confounding Variables
Multiple Confounding VariablesMultiple Confounding Variables
1. Define and Identify Confounding 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
OverviewOverview