Comparing restricted mean survival times in small sample clinical trials using pseudo-observations

TL;DR

This paper introduces pseudo-observation regression methods for comparing restricted mean survival times in small clinical trials, offering alternatives to traditional tests that often have inflated error rates.

Contribution

It proposes two novel pseudo-observation based methods and evaluates their performance against existing approaches through extensive simulations and real data application.

Findings

Pseudo-observation methods control type I error better in small samples.

Proposed methods show improved power over existing tests.

Application demonstrates practical utility with covariate adjustments.

Abstract

The widely used proportional hazard assumption cannot be assessed reliably in small-scale clinical trials and might often in fact be unjustified, e.g. due to delayed treatment effects. An alternative to the hazard ratio as effect measure is the difference in restricted mean survival time (RMST) that does not rely on model assumptions. Although an asymptotic test for two-sample comparisons of the RMST exists, it has been shown to suffer from an inflated type I error rate in samples of small or moderate sizes. Recently, permutation tests, including the studentized permutation test, have been introduced to address this issue. In this paper, we propose two methods based on pseudo-observations (PO) regression models as alternatives for such scenarios and assess their properties in comparison to previously proposed approaches in an extensive simulation study. Furthermore, we apply the proposed PO methods to data from a clinical trail and, by doing so, point out some extension that might be very useful for practical applications such as covariate adjustments.