Sample size for a non-inferiority clinical trial with time-to-event data in the presence of competing risks

TL;DR

This paper develops a sample size calculation method for non-inferiority clinical trials involving competing risks and demonstrates its effectiveness through simulations and an example.

Contribution

It introduces a novel sample size formula tailored for non-inferiority trials with competing risks using the proportional sub-distribution hazard model.

Findings

Empirical power reaches targeted levels with the proposed formula.

Simulation results validate the accuracy of the sample size calculation.

The method is applicable to trials with Weibull-distributed competing risks.

Abstract

The analysis and planning methods for competing risks model have been described in the literatures in recent decades, and non-inferiority clinical trials are helpful in current pharmaceutical practice. Analytical methods for non-inferiority clinical trials in the presence of competing risks were investigated by Parpia et al., who indicated that the proportional sub-distribution hazard model is appropriate in the context of biological studies. However, the analytical methods of competing risks model differ from those appropriate for analyzing non-inferiority clinical trials with a single outcome; thus, a corresponding method for planning such trials is necessary. A sample size formula for non-inferiority clinical trials in the presence of competing risks based on the proportional sub-distribution hazard model is presented in this paper. The primary endpoint relies on the sub-distribution hazard ratio. A total of 120 simulations and an example based on a randomized controlled trial verified the empirical performance of the presented formula. The results demonstrate that the empirical power of sample size formulas based on the Weibull distribution for non-inferiority clinical trials with competing risks can reach the targeted power.