Assessing effect heterogeneity of a randomized treatment using conditional inference trees

TL;DR

This paper introduces TEHTrees, a method combining prognostic score matching and conditional inference trees to detect and characterize treatment effect heterogeneity in randomized trials while controlling false positive rates.

Contribution

The paper presents a novel approach that controls Type I error in identifying effect heterogeneity, suitable for clinical trials, and demonstrates its effectiveness through simulations and real data.

Findings

TEHTrees effectively identify heterogeneous subgroups.

The method controls the Type I error rate.

Application to a nutrition trial illustrates practical utility.

Abstract

Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized binary treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, i.e., the probability of identifying effect heterogeneity if none exists and retains the underlying subgroups. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups. TEHTrees are able to identify heterogeneous subgroups, characterize the relevant subgroups and estimate the associated treatment effects. We demonstrate the efficacy of the proposed method using a comprehensive simulation study and illustrate our method using a nutrition trial dataset to evaluate effect heterogeneity within a patient population.