More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study

Abstract

AbstractAdjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the TCGA cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 1.7% when cumulative incidence is of 10% to 26.5% when cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of HCC trials, we find that the number of patients screened for eligibility can be divided by 2.7 when broadening eligibility criteria. Last, we find that the Cox-Snell is a good approximation of the reduction in sample size requirements provided by covariate adjustment. This metric can be used in the design of time-to-event trials to determine sample size. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers.Key messagesCovariate adjustment is a statistical technique that leverages prognostic scores within the statistical analysis of a trial. We study its benefits for time-to-event trials.Power gain achieved with covariate adjustment is determined by the prognostic performance of the covariate and by the cumulative incidence of events at the end of the follow-up period.Trials in indications with large cumulative incidence such as metastatic cancers can benefit from covariate adjustment to improve their statistical power.Covariate adjustment maintains statistical power in trials when eligibility criteria are broadened.