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Randomized controlled trials (RCTs) with dichotomous outcomes may be analyzed with or without adjustment for baseline characteristics (covariates). We studied type I error, power, and potential reduction in sample size with several covariate adjustment strategies.

Logistic regression analysis was applied to simulated data sets (n=360) with different treatment effects, covariate effects, outcome incidences, and covariate prevalences. Treatment effects were estimated with or without adjustment for a single dichotomous covariate. Strategies included always adjusting for the covariate (“prespecified”), or only when the covariate was predictive or imbalanced.

We found that the type I error was generally at the nominal level. The power was highest with prespecified adjustment. The potential reduction in sample size was higher with stronger covariate effects (from 3 …