Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements

J Clin Epidemiol. 2004 May;57(5):454-60. doi: 10.1016/j.jclinepi.2003.09.014.


Objective: 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.

Study design and setting: 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.

Results: 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 to 46%, at 50% outcome incidence and covariate prevalence) and independent of the treatment effect. At lower outcome incidences and/or covariate prevalences, the reduction was lower.

Conclusion: We conclude that adjustment for a predictive baseline characteristic may lead to a potentially important increase in power of analyses of treatment effect. Adjusted analysis should, hence, be considered more often for RCTs with dichotomous outcomes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Interpretation, Statistical
  • Humans
  • Logistic Models
  • Randomized Controlled Trials as Topic / methods*
  • Research Design
  • Sample Size
  • Treatment Outcome