Subgroup identification in dose-finding trials via model-based recursive partitioning

TL;DR

This paper introduces a novel approach using model-based recursive partitioning to identify patient subgroups with different dose-response behaviors in dose-finding trials, enhancing treatment effect estimation.

Contribution

It extends model-based recursive partitioning to dose-finding trials, addressing subgroup identification challenges unique to multiple dose groups.

Findings

Identifies patient subgroups with distinct dose-response curves.

Improves estimation of treatment effects in heterogeneous populations.

Enhances determination of minimum effective doses.

Abstract

An important task in early phase drug development is to identify patients, which respond better or worse to an experimental treatment. While a variety of different subgroup identification methods have been developed for the situation of trials that study an experimental treatment and control, much less work has been done in the situation when patients are randomized to different dose groups. In this article we propose new strategies to perform subgroup analyses in dose-finding trials and discuss the challenges, which arise in this new setting. We consider model-based recursive partitioning, which has recently been applied to subgroup identification in two arm trials, as a promising method to tackle these challenges and assess its viability using a real trial example and simulations. Our results show that model-based recursive partitioning can be used to identify subgroups of patients with different dose-response curves and improves estimation of treatment effects and minimum effective doses, when heterogeneity among patients is present.