Regression modelling and other methods to control confounding

Abstract

onfounding 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.