Confounding, Confounding Fact... Health Article

ADJUSTMENT FOR CONFOUNDING

Design-based methods are often infeasible or insufficient to prevent confounding. Thus, there has been an enormous amount of work devoted to analytic adjustments for confounding. With a few exceptions, these methods are based on observed covariate distributions in the compared populations. Such methods can successfully control confounding only to the extent that enough confounders are adequately measured. Then, too, many methods employ parametric models at some stage, and their success may thus depend on the faithfulness of the model to reality. These issues cannot be covered in depth here, but a few basic points are worth noting.

The simplest and most widely trusted methods of adjustment begin with stratification on confounders. A covariate cannot be responsible for confounding within internally homogeneous strata of the covariate. For example, gender imbalances cannot confound observations within a stratum composed solely of women, More generally, comparisons within strata cannot be confounded by a covariate that is unassociated with treatment within strata. This is so regardless of whether the covariate was used to define the strata. Thus, one need not stratify on all confounders in order to control confounding. Furthermore, if one has accurate background information on relations among the confounders, one may use this information to identify sets of covariates sufficient for control of confounding.

Some controversy has occurred about adjustment for covariates in randomized trials. Although Fisher asserted that randomized comparisons were "unbiased," he also pointed out that they could be confounded in the sense used here. Resolution comes from noting that Fisher's use of the word unbiased referred to the design and was not meant to guide analysis of a given trial. Once the trial is underway and the actual treatment allocation is completed, the unadjusted treatment-effect estimate will be biased if the covariate is associated with treatment, and this bias can be removed by adjustment for the covariate.

SANDER GREENLAND

(SEE ALSO: Bias)

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