Subgroup Identification and Individualized Treatment Policies: A Tutorial on the Hybrid Two-Stage Workflow

TL;DR

This paper introduces a hybrid two-stage workflow combining statistical inference and machine learning to identify patient subgroups and develop personalized treatment policies with statistical guarantees.

Contribution

It presents a novel integrated approach that combines effect heterogeneity testing with individualized treatment policy estimation, ensuring statistical validity and practical applicability.

Findings

Workflow effectively detects treatment effect heterogeneity.

Personalized policies outperform uniform treatment strategies.

Method demonstrated on simulated and real HIV trial data.

Abstract

Patients in clinical studies often exhibit heterogeneous treatment effect (HTE). Classical subgroup analyses provide inferential tools to test for effect modification, while modern machine learning methods estimate the Conditional Average Treatment Effect (CATE) to enable individual level prediction. Each paradigm has limitations: inference focused approaches may sacrifice predictive utility, and prediction focused approaches often lack statistical guarantees. We present a hybrid two-stage workflow that integrates these perspectives. Stage 1 applies statistical inference to test whether credible treatment effect heterogeneity exists with the protection against spurious findings. Stage 2 translates heterogeneity evidence into individualized treatment policies, evaluated by cross fitted doubly robust (DR) metrics with Neyman-Pearson (NP) constraints on harm. We illustrate the workflow with working examples based on simulated data and a real ACTG 175 HIV trial. This tutorial provides practical implementation checklists and discusses links to sponsor oriented HTE workflows, offering a transparent and auditable pathway from heterogeneity assessment to individualized treatment policies.