A Clustering Approach for Basket Trials Based on Treatment Response Trajectories

TL;DR

This paper introduces a model-free clustering method for basket trials that groups baskets based on treatment response trajectories, improving efficacy estimation and statistical power in heterogeneous settings.

Contribution

It proposes a novel trajectory-based clustering framework with data-driven cluster determination, enhancing analysis of basket trial heterogeneity.

Findings

Accurately identifies cluster structures in simulations.

Maintains type I error rate at nominal level.

Improves statistical power in heterogeneous scenarios.

Abstract

Heterogeneity in efficacy is sometimes observed across baskets in basket trials. In this study, we propose a model-free clustering framework that groups baskets based on transition probabilities derived from the trajectories of treatment response, rather than relying solely on a single efficacy endpoint such as the objective response rate. The number of clusters is not predetermined but is automatically determined in a data-driven manner based on the similarity structure among baskets. After clustering, baskets within the same cluster are analyzed using a hierarchical Bayesian model. This framework aims to improve the estimation precision of efficacy endpoints and enhance statistical power while maintaining the type~I error rate at the nominal level. The performance of the proposed method was evaluated through simulation studies. The results demonstrated that the proposed method can accurately identify cluster structures in heterogeneous settings and, even under such conditions, maintain the type~I error rate at the nominal level while improving statistical power.