How to select covariates to include in the analysis of a clinical trial

The comparisons of treatments in randomized clinical trials may use the ana lysis of covariance to adjust for patient characteristics. We present theor etical results that describe when such an adjustment would be expected to b e beneficial. A distinction is made between covariates that are balanced in the design and those that are assigned by the randomization process. The r esults support the commonly held view that features balanced in the design of the trial (e.g., by stratification) and those that are strongly predicti ve of the outcome, and thus considered clinically prognostic, should normal ly be included in the analysis. For other covariates that are not balanced in the design, the potential benefits of including them in the analysis wil l depend on the number of patients in the trial. However, there is frequent ly a set of variables whose relevance is unknown and for which data-depende nt methods of selection, based on the data for the current trial, have been proposed. A review of the literature has shown that these methods can prod uce misleading inferences. The decision as to which covariates to include i n the analysis should be specified in the protocol on the basis of data fro m previous trials on similar patient populations. The methods are illustrat ed with data from a trial comparing two therapies for treating scalp psoria sis where the clinical importance of patients' age and sex as prognostic fa ctors for efficacy is unknown. We show for what size of future trials it wo uld be beneficial to adjust for these covariates and for what size trials i t would not. In all cases, prespecification of variables to be included in the analysis is essential in order to avoid bias. (C) Elsevier Science Inc. 2000.