title: The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies.
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.
authors.affiliation: Pragmatic Clinical Trials Unit, Queen Mary University of London, London E1 2AB, UK. b.kahan@qmul.ac.uk.
authors.forename: Brennan C
authors.initials: BC
authors.lastname: Kahan
authors.forename: Vipul
authors.initials: V
authors.lastname: Jairath
authors.forename: Caroline J
authors.initials: CJ
authors.lastname: Doré
authors.forename: Tim P
authors.initials: TP
authors.lastname: Morris
mesh_terms: D000704:Analysis of Variance; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012107:Research Design / Q000706:statistics & numerical data*
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