# Sample Size Recalculation in Adaptive Group Sequential Study Designs for Comparing Restricted Mean Survival Times

## TL;DR

This paper introduces a method for adjusting sample sizes in clinical trials when comparing survival times, especially when treatment effects are delayed.

## Contribution

The novel contribution is an adaptive design using restricted mean survival time as the primary endpoint with delayed treatment effects.

## Key findings

- Adaptive designs with restricted mean survival time can handle delayed treatment effects effectively.
- Simulation studies show the proposed method performs well compared to other endpoints.
- The combination test described can be used for various adaptations beyond sample size.

## Abstract

Non‐proportional hazards cases are frequently expected in clinical trials with time‐to‐event endpoints (e.g., cardiology, oncology). The relevance of hazard ratios to quantify the treatment effect is questionable and potentially misleading in this context. Hence, alternative methods comparing restricted mean survival times are increasingly promoted. Specific challenges arise when planning clinical trials for comparing restricted mean survival times, as several nuisance parameter estimates are needed for calculating the sample size. Precise estimates might be difficult to obtain at the planning stage and might lead to underpowered trials. One way of dealing with this insecurity is to apply adaptive group sequential study designs with the option to adapt the sample size during an ongoing trial. Within this work, we consider such sample size adaptations, with a specific focus on the context of delayed treatment effects. We compare the performance of an adaptive design with the restricted mean survival time as the primary endpoint with other commonly chosen endpoints in this scenario by means of an extensive simulation study. With our proposed method, adaptive designs with the restricted mean survival time as the primary endpoint are now thoroughly explained. The combination test that we describe can also be useful for other adaptations than sample sizes.