EDUCATION

Regression modelling and other methods to control confounding

R McNamee

Correspondence to:
Correspondence to:
Dr R McNamee
Biostatistics Group, Division of Epidemiology and Health Sciences, Faculty of Medical and Human Sciences, The University of Manchester, Oxford Road, Manchester M13 9PT, UK; rmcnamee@man.ac.uk

Received for publication 22 November 2004.

Keywords: regression; confounder control; regression; statistical modelling; stratification; method comparison

The first 150 words of the full text of this article appear below.

Confounding is a major concern in causal studies because it results in biased estimation of exposure effects. In the extreme, this can mean that a causal effect is suggested where none exists, or that a true effect is hidden. Typically, confounding occurs when there are differences between the exposed and unexposed groups in respect of independent risk factors for the disease of interest, for example, age or smoking habit; these independent factors are called confounders. Confounding can be reduced by matching in the study design but this can be difficult and/or wasteful of resources. Another possible approach—assuming data on the confounder(s) have been gathered—is to apply a statistical "correction" method during analysis. Such methods produce "adjusted" or "corrected" estimates of the effect of exposure; in theory, these estimates are no longer biased by the erstwhile confounders.

Given the importance of confounding in epidemiology, statistical methods said to remove it deserve . . . [Full text of this article]

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