Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

Autor:	Yang J; Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA., Mwangi AW; Department of Mathematics, Physics and Computing, School of Science and Aerospace Studies, Moi University, Eldoret 30100, Kenya.; Academic Model Providing Access to Healthcare (AMPATH), Eldoret 30100, Kenya., Kantor R; Division of Infectious Diseases, Warren Alpert Medical School, Brown University, Providence, RI 02903, USA., Dahabreh IJ; CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA., Nyambura M; Academic Model Providing Access to Healthcare (AMPATH), Eldoret 30100, Kenya., Delong A; Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA.; Center for Statistical Sciences, School of Public Health, Brown University, Providence, RI 02903, USA., Hogan JW; Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA.; Center for Statistical Sciences, School of Public Health, Brown University, Providence, RI 02903, USA., Steingrimsson JA; Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA.; Center for Statistical Sciences, School of Public Health, Brown University, Providence, RI 02903, USA.
Jazyk:	angličtina
Zdroj:	Biostatistics (Oxford, England) [Biostatistics] 2024 Apr 15; Vol. 25 (2), pp. 323-335.
DOI:	10.1093/biostatistics/kxad014
Abstrakt:	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 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 who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs. (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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