Treatment Effect Quantification for Time-to-event Endpoints -- Estimands, Analysis Strategies, and beyond

TL;DR

This paper discusses the integration of estimand framework into time-to-event endpoint analysis in clinical trials, highlighting issues with common methods under non-proportional hazards and proposing a comprehensive approach for future trial planning.

Contribution

It is the first to analyze how the estimand framework applies to time-to-event endpoints and to relate analysis strategies to the ICH E9 addendum in a real trial context.

Findings

Logrank test and Cox regression depend on censoring if proportional hazards assumption fails.

Analysis strategies can be aligned with estimand framework for better trial design.

Recommendations for future trial planning based on estimand considerations.

Abstract

A draft addendum to ICH E9 has been released for public consultation in August 2017. The addendum focuses on two topics particularly relevant for randomized confirmatory clinical trials: estimands and sensitivity analyses. The need to amend ICH E9 grew out of the realization of a lack of alignment between the objectives of a clinical trial stated in the protocol and the accompanying quantification of the "treatment effect" reported in a regulatory submission. We embed time-to-event endpoints in the estimand framework, and discuss how the four estimand attributes described in the addendum apply to time-to-event endpoints. We point out that if the proportional hazards assumption is not met, the estimand targeted by the most prevalent methods used to analyze time-to-event endpoints, logrank test and Cox regression, depends on the censoring distribution. We discuss for a large randomized clinical trial how the analyses for the primary and secondary endpoints as well as the sensitivity analyses actually performed in the trial can be seen in the context of the addendum. To the best of our knowledge, this is the first attempt to do so for a trial with a time-to-event endpoint. Questions that remain open with the addendum for time-to-event endpoints and beyond are formulated, and recommendations for planning of future trials are given. We hope that this will provide a contribution to developing a common framework based on the final version of the addendum that can be applied to design, protocols, statistical analysis plans, and clinical study reports in the future.