Confirmatory adaptive group sequential designs for clinical trials with multiple time-to-event outcomes in Markov models

TL;DR

This paper introduces a flexible adaptive group sequential design for clinical trials with multiple time-to-event outcomes, utilizing Markov models to incorporate disease history and enable interim adaptations, especially for oncology studies.

Contribution

It proposes a novel method embedding endpoints in Markov models to allow data-dependent design adaptations in multi-outcome clinical trials.

Findings

Simulation studies show good properties for small sample sizes.

Application to NB2004-HR data demonstrates practical utility.

Method effectively accounts for disease history in interim analyses.

Abstract

The analysis of multiple time-to-event outcomes in a randomised controlled clinical trial can be accomplished with exisiting methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multi-state model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data-dependent design adaptiations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression-free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004-HR study.