Heterogeneous treatment effect analysis based on machine‐learning methodology

Abstract Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE‐informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE an...

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Autores principales:	Xiajing Gong, Meng Hu, Mahashweta Basu, Liang Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/e940d1e300514556aecec097f696b4a6
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spelling	oai:doaj.org-article:e940d1e300514556aecec097f696b4a62021-11-15T18:41:54ZHeterogeneous treatment effect analysis based on machine‐learning methodology2163-830610.1002/psp4.12715https://doaj.org/article/e940d1e300514556aecec097f696b4a62021-11-01T00:00:00Zhttps://doi.org/10.1002/psp4.12715https://doaj.org/toc/2163-8306Abstract Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE‐informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine‐learning algorithm. We conducted a systematic performance evaluation for the causal forest method against the conventional two‐step method by simulating scenarios with different levels of complexity for the analysis. Our results show that causal forest outperforms the conventional HTE method in assessing treatment effect, especially when data are complex (e.g., nonlinear) and high dimensional, suggesting that causal forest is a promising tool for real‐world applications of HTE analysis.Xiajing GongMeng HuMahashweta BasuLiang ZhaoWileyarticleTherapeutics. PharmacologyRM1-950ENCPT: Pharmacometrics & Systems Pharmacology, Vol 10, Iss 11, Pp 1433-1443 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Therapeutics. Pharmacology RM1-950
spellingShingle	Therapeutics. Pharmacology RM1-950 Xiajing Gong Meng Hu Mahashweta Basu Liang Zhao Heterogeneous treatment effect analysis based on machine‐learning methodology
description	Abstract Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE‐informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine‐learning algorithm. We conducted a systematic performance evaluation for the causal forest method against the conventional two‐step method by simulating scenarios with different levels of complexity for the analysis. Our results show that causal forest outperforms the conventional HTE method in assessing treatment effect, especially when data are complex (e.g., nonlinear) and high dimensional, suggesting that causal forest is a promising tool for real‐world applications of HTE analysis.
format	article
author	Xiajing Gong Meng Hu Mahashweta Basu Liang Zhao
author_facet	Xiajing Gong Meng Hu Mahashweta Basu Liang Zhao
author_sort	Xiajing Gong
title	Heterogeneous treatment effect analysis based on machine‐learning methodology
title_short	Heterogeneous treatment effect analysis based on machine‐learning methodology
title_full	Heterogeneous treatment effect analysis based on machine‐learning methodology
title_fullStr	Heterogeneous treatment effect analysis based on machine‐learning methodology
title_full_unstemmed	Heterogeneous treatment effect analysis based on machine‐learning methodology
title_sort	heterogeneous treatment effect analysis based on machine‐learning methodology
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/e940d1e300514556aecec097f696b4a6
work_keys_str_mv	AT xiajinggong heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology AT menghu heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology AT mahashwetabasu heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology AT liangzhao heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology
_version_	1718426878659788800

Heterogeneous treatment effect analysis based on machine‐learning methodology

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