Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects

TL;DR

This paper introduces an interpretable machine learning method based on RuleFit for estimating heterogeneous treatment effects in survival data, addressing the need for transparency and applicability in medical research.

Contribution

It develops a novel survival analysis approach that combines interpretability with high prediction accuracy for heterogeneous treatment effects.

Findings

Prediction performance comparable to existing methods

Effective interpretability demonstrated with real HIV data

Model suitable for precision medicine applications

Abstract

With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with different characteristics are always heterogeneous, and various heterogeneous treatment effect machine learning estimation methods have been proposed owing to their flexibility and high prediction accuracy. However, most machine learning methods rely on black-box models, preventing direct interpretation of the relationship between patient characteristics and treatment effects. Moreover, most of these studies have focused on continuous or binary outcomes, although survival outcomes are also important in medical research. To address these challenges, we propose a heterogeneous treatment effect estimation method for survival data based on RuleFit, an interpretable machine learning method. Numerical simulation results confirmed that the prediction performance of the proposed method was comparable to that of existing methods. We also applied a dataset from an HIV study, the AIDS Clinical Trials Group Protocol 175 dataset, to illustrate the interpretability of the proposed method using real data. Consequently, the proposed method established an interpretable model with sufficient prediction accuracy.