A Milestone-Based Framework for Characterizing Time-Varying Treatment Effects in Immunotherapy Trials

TL;DR

This paper introduces a milestone-based framework to better characterize and interpret the evolving treatment effects over time in immunotherapy trials, addressing limitations of traditional measures.

Contribution

It proposes a novel milestone-based approach that separates long-term survival from early outcomes, capturing heterogeneity and time-varying effects more effectively.

Findings

Framework captures complex survival patterns in immunotherapy trials.

Illustrated with data from three landmark phase III trials.

Helps identify when treatment benefits emerge and how effects change over time.

Abstract

Immune checkpoint inhibitor--based therapies often produce heterogeneous survival responses, including early risk, delayed treatment benefit, and durable long-term survival in a subset of patients. In these settings, conventional summary measures such as the hazard ratio may not adequately describe how treatment effects evolve over follow-up. We propose a milestone-based framework that separates long-term survival beyond a clinically meaningful time point from earlier outcomes and provides a practical way to characterize patient heterogeneity in treatment response. The framework summarizes treatment differences through milestone survival probabilities and, among patients who do not reach the milestone, characterizes short-term treatment ordering over time using a tau-based summary that helps identify hazard reversal. We illustrate the approach using reconstructed individual-level data from three landmark phase III trials: CheckMate~067, CheckMate~227, and CLEAR. Across these examples, the framework captures patterns that are difficult to summarize with conventional measures, including settings in which early disadvantage coexists with later durable benefit. It also helps clarify when treatment benefit begins to emerge and how short-term and long-term effects differ within the same trial. This approach provides a clinically interpretable and statistically principled way to evaluate heterogeneous and time-varying treatment effects in oncology trials with nonproportional hazards.