Design and Analysis of group-sequential clinical trials based on a modestly-weighted log-rank test in anticipation of a delayed separation of survival curves: A practical guidance

TL;DR

This paper offers practical guidance on designing and analyzing group-sequential clinical trials using a modestly-weighted log-rank test to better detect delayed survival benefits, especially in immunotherapy studies.

Contribution

It introduces a new design approach with step-by-step analysis instructions and provides an R package for implementation, addressing a gap in practical guidance for delayed effect trials.

Findings

Re-design of the POPLAR trial for delayed effects

Step-by-step analysis procedure demonstrated

Provision of an R package for practitioners

Abstract

A common feature of many recent trials evaluating the effects of immunotherapy on survival is that non-proportional hazards can be anticipated at the design stage. This raises the possibility to use a statistical method tailored towards testing the purported long-term benefit, rather than applying the more standard log-rank test and/or Cox model. Many such proposals have been made in recent years, but there remains a lack of practical guidance on implementation, particularly in the context of group-sequential designs. In this article, we aim to fill this gap. We discuss how the POPLAR trial, which compared immunotherapy versus chemotherapy in non-small-cell lung cancer, might have been re-designed to be more robust to the presence of a delayed effect. We then provide step-by-step instructions on how to analyse a hypothetical realisation of the trial, based on this new design. Basic theory on weighted log-rank tests and group-sequential methods is covered, and an accompanying R package (including vignette) is provided.