To the Editor: Keech and Gebski recently discussed some strategies for answering randomised clinical trial (RCT) questions with fewer subjects.1 We would like to point out another alternative for addressing this important topic — adjustment for baseline characteristics.2-4

Heterogeneity among patients participating in RCTs is common. Prognosis may vary according to important baseline characteristics, which are commonly recorded in RCTs. Heterogeneity may lead to imbalanced treatment arms, even after proper randomisation.3

Covariate adjustment for baseline characteristics is a statistically efficient procedure. It leads to more individualised treatment-effect estimates, corrects for imbalance and improves statistical power.2,3 Hence, it may potentially reduce the necessary sample size of an RCT for the same power as unadjusted analyses. Nevertheless, covariate adjustment is not commonly performed in the RCTs reported in major medical journals.5

We recently performed a simulation study using logistic regression models in the context of RCTs with dichotomous outcomes and one simple dichotomous baseline characteristic in addition to the treatment indicator variable. Covariate adjustment was found to potentially reduce the sample size between 3% and 46%, in direct relation to the strength of the baseline characteristic (odds ratio, 2 to 30). Results of a simulation study in RCTs with survival outcomes, using Cox proportional hazards models, yielded similar results.

Covariate adjustment for well-known and important predictors of patient prognosis is a useful tool for potentially reducing the sample size of RCTs, and should be considered more often in their design and analysis.