Covariate adjustment had similar benefits in small and large randomized controlled trials

Abstract

Objectives: Covariate 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 setting: We 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 < 0.05 vs. P ≥ 0.05).

Results: The 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.

Conclusion: The 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.

Keywords: Chance imbalance; Covariate adjustment; Logistic regression analysis; Randomized trial; Sample size; Simulation.