Adaptive Survival Trials

TL;DR

This paper addresses the challenge of designing adaptive survival trials with time-to-event endpoints, proposing a new method that incorporates all event times to improve accuracy while controlling error rates.

Contribution

It introduces an alternative test for adaptive survival trials that includes all event times, enhancing the analysis of long-term survival data.

Findings

The proposed method controls type I error effectively.

It improves the use of interim data in survival analysis.

Application to cancer therapy trial demonstrates practical benefits.

Abstract

Mid-study design modifications are becoming increasingly accepted in confirmatory clinical trials, so long as appropriate methods are applied such that error rates are controlled. It is therefore unfortunate that the important case of time-to-event endpoints is not easily handled by the standard theory. We analyze current methods that allow design modifications to be based on the full interim data, i.e., not only the observed event times but also secondary endpoint and safety data from patients who are yet to have an event. We show that the final test statistic may ignore a substantial subset of the observed event times. Since it is the data corresponding to the earliest recruited patients that is ignored, this neglect becomes egregious when there is specific interest in learning about long-term survival. An alternative test incorporating all event times is proposed, where a conservative assumption is made in order to guarantee type I error control. We examine the properties of our proposed approach using the example of a clinical trial comparing two cancer therapies.