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

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

Definition:


Risk factor that has different prevalence intwo study populations…

e.g. Coffee drinking and lung cancer

Why does this happen?


Men vs Women Example….Men vs Women Example….

25% Risk of lung cancer

5% Risk of Lung Cancer

ExampleExample




ExampleExample

Conclusion: People who drink coffee die more therefore coffee causes 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





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 is the correct measure of association?

ExampleExample


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


Cancer+ Cancer-

Coffee+ 178 1129

Coffee- 79 1262

ExampleExample

Data

Well??


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


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-



Compute Risk Ratios for Both, Separately


Smoker- Cancer+ Cancer-

Coffee+

Coffee-

Smoker+ Cancer+ Cancer-

Coffee+

Coffee-

Calculate the adjusted measure of association


Stratified data:Smoker+


Stratified data:Smoker-


2. Compute Risk Ratios for Both, Separately


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


Adjusted Ratio Must be >10% Different than the Crude Ratio

Adjusted Ratio Must be >10% Different than the Crude Ratio


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.



Issues with ConfoundingIssues with Confounding





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


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.


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.


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.





Many issues with this method.Many issues with this method.



What is significant?What is significant?





Many issues with this method.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!

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


Caveats Caveats

Need to control for as few confounding variables as possible.



Caveats Caveats



You are limited by the number of cases of the outcome you have (10:1 Rule)



Caveats Caveats





Some journals just want it done a certain way.

Some journals just want it done a certain way.



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

tim wiemken phd mph cic assistant professor division of infectious diseases

Documents

coffee cancer

coffee cancer

smoker coffee cancer

smokercoffee cancer

coffee drinking

higher risk of lung

lot of coffee drinkers

didnt drink coffee