Strategies to boost statistical efficiency in randomized oncology trials with primary time-to-event endpoints

Abstract

Oncology clinical trials are increasingly expensive, necessitating efforts to streamline phase II and III trials to reduce costs and expedite treatment delivery. Randomization is often impractical in oncology trials due to small sample sizes and limited statistical power, leading to biased inferences. The FDA has recently published guidance documents encouraging the use of prognostic baseline measures to improve the precision of inferences around treatment effects. To address this, we propose an extension of Rosenbaum’s exact testing method incorporating a variant of martingale residuals for right censored data. This method can dramatically improve the statistical power of the test comparing treatment arms given time-to-event endpoints as compared to the standard log-rank test. Additionally, the modification of the martingale residual provides a straightforward metric for summarizing treatment effect by quantifying the expected events per treatment arm at each time-point. This approach is illustrated using a phase II clinical trial in small cell lung cancer.