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

arXiv: https://arxiv.org/abs/2208.14329 · HTML: https://mlsanity.com/paper/2208.14329/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Optimal Inference After Model Selection](https://mlsanity.com/paper/1410.2597/) (2014)
- [Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables](https://mlsanity.com/paper/1302.4977/) (2013)
- [Model-based Recursive Partitioning for Subgroup Analyses](https://mlsanity.com/paper/1605.04078/) (2016)
- [Causal Interaction Trees: Tree-Based Subgroup Identification for Observational Data](https://mlsanity.com/paper/2003.03042/) (2020)
- [Subgroup Identification using Covariate Adjusted Interaction Trees](https://mlsanity.com/paper/1806.08258/) (2018)

## Built on by (most cited first, 2 of 2)

- [SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification](https://mlsanity.com/paper/2401.12369/) (2024)
- [Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder](https://mlsanity.com/paper/2401.17027/) (2024)

## Abstract (arXiv)

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.