Model-based Recursive Partitioning for Subgroup Analyses

TL;DR

This paper presents a model-based recursive partitioning method to automatically identify patient subgroups with different treatment effects, aiding personalized medicine and addressing challenges in subgroup analysis.

Contribution

It introduces a novel statistical procedure that detects subgroups via parameter instability measures, linking them to predictive factors through decision trees.

Findings

Successfully identified ALS patient subgroups with varying Riluzole effects

Automated detection of subgroups improves personalized treatment strategies

Method aligns with EMA guidelines for subgroup analysis

Abstract

The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.