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Abstract
This article discusses the importance, definition, and types of confounders in epidemiology. Methods to identify and address confounding are discussed, as well as their strengths and limitations. The article also describes the difference among confounders, mediators, and effect modifiers.
Keywords: Confounder, effect modifier, interaction, mediator, randomization, regression
Introduction
In epidemiology, like other fields of science, we look for causes of diseases, by which we mean exposures that change the risk of diseases. For example, by "smoking causes lung cancer", we mean smoking increases the risk of lung cancer; lifetime risk of lung cancer is 17% in male smokers versus 1% in male non-smokers.1 Once we find that smoking causes lung cancer, people are encouraged not to smoke and public policies are made.
Whereas causation always results in a change in risk, the converse is not necessarily true. Increased risk of a health outcome in the presence of an exposure doesn't necessarily imply a causal relationship between the exposure and outcome. One reason for such non-causal associations is the presence of a third variable called confounder or confounding variable. See the example below.
Example 1: Some epidemiologic studies have found that poor oral health and/or tooth loss is associated with an increased risk of esophageal cancer.2,3 But does this mean that poor oral health causes esophageal cancer? Maybe yes. But maybe there are other factors (e.g., smoking) behind the scene. Smoking causes poor oral health and it also causes esophageal cancer. Therefore, an association between tooth loss (the exposure) and esophageal cancer (the outcome) may be due to smoking (a confounder).
In this article, we discuss the following topics:
1) Criteria for confounding;
2) Types confounders;
3) Surrogate confounders;
4) Stratification as a method to understand confounders;
5) Confounders versus other "third" variables (mediators and effect modifiers);
6) Confounding versus selection bias;
7) Confounding by indication;
8) How to identify potential confounders;
9) Methods used to address confounders;
10) Deficiencies of methods used to address confounders;
11) Overadjustment; and
12) How strongly can the confounders distort the associations.
In the final part, summary and conclusions, we tie these 12 topics together and provide a framework for thinking about and handling confounders.
1. Criteria for confounding
A confounder is a variable that distorts the...