The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

Trials. 2014 Apr 23;15:139. doi: 10.1186/1745-6215-15-139.

Abstract

Background: Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice.

Methods: We used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of therapeutic areas. We assessed (1) how large an increase in power can be expected in practice; and (2) the impact of adjustment for covariates that are not prognostic.

Results: Adjustment for known prognostic covariates led to large increases in power for most outcomes. When power was set to 80% based on an unadjusted analysis, covariate adjustment led to a median increase in power to 92.6% across the 12 outcomes (range 80.6 to 99.4%). Power was increased to over 85% for 8 of 12 outcomes, and to over 95% for 5 of 12 outcomes. Conversely, the largest decrease in power from adjustment for covariates that were not prognostic was from 80% to 78.5%.

Conclusions: Adjustment for known prognostic covariates can lead to substantial increases in power, and should be routinely incorporated into the analysis of randomized trials. The potential benefits of adjusting for a small number of possibly prognostic covariates in trials with moderate or large sample sizes far outweigh the risks of doing so, and so should also be considered.

MeSH terms

  • Analysis of Variance
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Humans
  • Linear Models
  • Logistic Models
  • Models, Statistical*
  • Proportional Hazards Models
  • Randomized Controlled Trials as Topic / statistics & numerical data*
  • Reproducibility of Results
  • Research Design / statistics & numerical data*