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ObjectivesCovariate adjustment is a standard statistical approach in the analysis of randomized controlled trials. We aimed to explore whether the benefit of covariate adjustment on statistical significance and power differed between small and large trials, where chance imbalance in prognostic factors necessarily differs.Study design and settingWe studied two large trial data sets [Global Use of Strategies to Open Occluded Coronary Arteries (GUSTO-I), N = 30,510 and International Stroke Trial (IST), N = 18,372] repeatedly drawing random samples (500,000 times) of sizes 300 and 5,000 per arm and simulated each primary outcome using the control arms. We empirically determined the treatment effects required to fix power at 80% for all unadjusted analyses and calculated the joint probabilities in the discordant cells when cross-classifying adjusted and unadjusted results from logistic regression models (ie, P ResultsThe power gained from an adjusted analysis for small and large samples was between 5% and 6%. Similar proportions of discordance were noted irrespective of the sample size in both the GUSTO-I and the IST data sets.ConclusionThe proportions of change in statistical significance from covariate adjustment of strongly prognostic characteristics were the same for small and large trials with similar gains in statistical power. Covariate adjustment is equally recommendable in small and large trials.

Original publication




Journal article


Journal of clinical epidemiology

Publication Date





1068 - 1075


Edinburgh Hub for Trials Methodology Research, Centre for Population Health Sciences University of Edinburgh, Edinburgh EH89AG, UK. Electronic address:


Humans, Brain Ischemia, Myocardial Infarction, Models, Statistical, Sample Size, Research Design, Computer Simulation, Aged, Aged, 80 and over, Middle Aged, Female, Male, Randomized Controlled Trials as Topic, Stroke, Outcome Assessment, Health Care