Effect measures for comparing paired event times

TL;DR

This paper introduces new reliable statistical methods for comparing paired event times in clinical trials, addressing the unreliability of existing tests and improving inference accuracy in personalized oncology studies.

Contribution

It develops novel inference procedures for the PFS ratio and restricted mean survival times, ensuring valid type I error control in paired survival data analysis.

Findings

Proposed methods reliably control type I error in simulations

Existing techniques often fail under realistic conditions

Application to tumor trial data demonstrates practical utility

Abstract

The progression-free survival ratio (PFSr) is a widely used measure in personalized oncology trials. It evaluates the effectiveness of treatment by comparing two consecutive event times - one under standard therapy and one under an experimental treatment. However, most proposed tests based on the PFSr cannot control the nominal type I error rate, even under mild assumptions such as random right-censoring. Consequently the results of these tests are often unreliable. As a remedy, we propose to estimate the relevant probabilities related to the PFSr by adapting recently developed methodology for the relative treatment effect between paired event times. As an additional alternative, we develop inference procedures based on differences and ratios of restricted mean survival times. An extensive simulation study confirms that the proposed novel methodology provides reliable inference, whereas previously proposed techniques break down in many realistic settings. The utility of our methods is further illustrated through an analysis of real data from a molecularly aided tumor trial.