Semiparametric Piecewise Accelerated Failure Time Model for the Analysis of Immune-Oncology Clinical Trials

TL;DR

This paper introduces a semiparametric piecewise accelerated failure time model tailored for immune-oncology trials, effectively capturing lag-time effects and identifying patients less likely to benefit from treatment.

Contribution

It proposes a novel semiparametric model that jointly estimates treatment effects and patient benefit profiles, addressing lag-time issues in immune-oncology survival analysis.

Findings

Parameters estimated with minimal bias in simulations

Model effectively characterizes lag-time effects

Real data analysis demonstrates practical utility

Abstract

Effectiveness of immune-oncology chemotherapies has been presented in recent clinical trials. The Kaplan-Meier estimates of the survival functions of the immune therapy and the control often suggested the presence of the lag-time until the immune therapy began to act. It implies the use of hazard ratio under the proportional hazards assumption would not be appealing, and many alternatives have been investigated such as the restricted mean survival time. In addition to such overall summary of the treatment contrast, the lag-time is also an important feature of the treatment effect. Identical survival functions up to the lag-time implies patients who are likely to die before the lag-time would not benefit the treatment and identifying such patients would be very important. We propose the semiparametric piecewise accelerated failure time model and its inference procedure based on the semiparametric maximum likelihood method. It provides not only an overall treatment summary, but also a framework to identify patients who have less benefit from the immune-therapy in a unified way. Numerical experiments confirm that each parameter can be estimated with minimal bias. Through a real data analysis, we illustrate the evaluation of the effect of immune-oncology therapy and the characterization of covariates in which patients are unlikely to receive the benefit of treatment.