Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies

TL;DR

This paper introduces SDLD, a novel tree-based algorithm that leverages longitudinal EHR data to identify subgroups with heterogeneous treatment effects, specifically applied to HIV patients on different ARTs.

Contribution

The paper presents the SDLD algorithm, combining generalized interaction trees with longitudinal targeted maximum likelihood estimation for subgroup discovery in longitudinal data.

Findings

Identified subgroups of HIV patients with varying weight gain risks.

Demonstrated the effectiveness of SDLD in analyzing EHR data.

Revealed heterogeneity in treatment effects for DTG-containing therapies.

Abstract

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD) algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus (HIV) who are at higher risk of weight gain when receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when receiving non dolutegravir-containing ARTs.