Studentized Permutation Method for Comparing Restricted Mean Survival Times with Small Sample from Randomized Trials

TL;DR

This paper introduces a studentized permutation test and confidence intervals for comparing restricted mean survival times in small, unbalanced clinical trial samples, addressing limitations of existing methods.

Contribution

The authors develop a studentized permutation approach for RMST comparison that works with non-exchangeable data and allows for valid confidence intervals, improving small sample analysis.

Findings

Enhanced control of type-I error in small samples

Effective in unbalanced group scenarios

Validated through simulation and real data analysis

Abstract

Recent observations, especially in cancer immunotherapy clinical trials with time-to-event outcomes, show that the commonly used proportial hazard assumption is often not justifiable, hampering an appropriate analyse of the data by hazard ratios. An attractive alternative advocated is given by the restricted mean survival time (RMST), which does not rely on any model assumption and can always be interpreted intuitively. As pointed out recently by Horiguchi and Uno (2020), methods for the RMST based on asymptotic theory suffer from inflated type-I error under small sample sizes. To overcome this problem, they suggested a permutation strategy leading to more convincing results in simulations. However, their proposal requires an exchangeable data set-up between comparison groups which may be limiting in practice. In addition, it is not possible to invert their testing procedure to obtain valid confidence intervals, which can provide more in-depth information. In this paper, we address these limitations by proposing a studentized permutation test as well as the corresponding permutation-based confidence intervals. In our extensive simulation study, we demonstrate the advantage of our new method, especially in situations with relative small sample sizes and unbalanced groups. Finally we illustrate the application of the proposed method by re-analysing data from a recent lung cancer clinical trial.