2006Unpublished venueRequires access

Confounding: What It Is and What Can Be Done

Ezra Susser, Sharon Schwartz, Alfredo Morabia, Evelyn J. Bromet

Open publisher page 2 citations

Abstract

Abstract The first part of this chapter discusses the conditions under which a factor can confound the association between exposure and disease, and the conditions under which this cannot occur. It also differentiates confounders from antecedents or mediators. The next part discusses methods devised to neutralize the effects of confounders. Two standard methods are presented: matching to prevent confounding in the data by equalizing the exposed and the unexposed on a potential confounder, and statistical adjustment to compensate for confounding in the data by separating the effects of the exposure from the effects of the confounder.

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What this paper is about

Abstract The first part of this chapter discusses the conditions under which a factor can confound the association between exposure and disease, and the conditions under which this cannot occur. It also differentiates confounders from antecedents or mediators. The next part discusses methods devised to neutralize the effects of confounders. Two standard methods are presented: matching to prevent confounding in the data by equalizing the exposed and the unexposed on a potential confounder, and statistical adjustment to compensate for confounding in the data by separating the effects of the exposure from the effects of the confounder.

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Available abstract

Abstract The first part of this chapter discusses the conditions under which a factor can confound the association between exposure and disease, and the conditions under which this cannot occur. It also differentiates confounders from antecedents or mediators. The next part discusses methods devised to neutralize the effects of confounders. Two standard methods are presented: matching to prevent confounding in the data by equalizing the exposed and the unexposed on a potential confounder, and statistical adjustment to compensate for confounding in the data by separating the effects of the exposure from the effects of the confounder.

Key concepts: Confounding, Matching (statistics), Medicine, Environmental health, Statistics, Psychology, Mathematics

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