Cargando…

Heterogeneous treatment effect analysis based on machine‐learning methodology

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 ha...

Descripción completa

Detalles Bibliográficos
Autores principales: Gong, Xiajing, Hu, Meng, Basu, Mahashweta, Zhao, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592515/
https://www.ncbi.nlm.nih.gov/pubmed/34716669
http://dx.doi.org/10.1002/psp4.12715
_version_ 1784599481016123392
author Gong, Xiajing
Hu, Meng
Basu, Mahashweta
Zhao, Liang
author_facet Gong, Xiajing
Hu, Meng
Basu, Mahashweta
Zhao, Liang
author_sort Gong, Xiajing
collection PubMed
description 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 Online
Article
Text
id pubmed-8592515
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-85925152021-11-22 Heterogeneous treatment effect analysis based on machine‐learning methodology Gong, Xiajing Hu, Meng Basu, Mahashweta Zhao, Liang CPT Pharmacometrics Syst Pharmacol Research 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. John Wiley and Sons Inc. 2021-10-30 2021-11 /pmc/articles/PMC8592515/ /pubmed/34716669 http://dx.doi.org/10.1002/psp4.12715 Text en © 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research
Gong, Xiajing
Hu, Meng
Basu, Mahashweta
Zhao, Liang
Heterogeneous treatment effect analysis based on machine‐learning methodology
title 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_short Heterogeneous treatment effect analysis based on machine‐learning methodology
title_sort heterogeneous treatment effect analysis based on machine‐learning methodology
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592515/
https://www.ncbi.nlm.nih.gov/pubmed/34716669
http://dx.doi.org/10.1002/psp4.12715
work_keys_str_mv AT gongxiajing heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology
AT humeng heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology
AT basumahashweta heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology
AT zhaoliang heterogeneoustreatmenteffectanalysisbasedonmachinelearningmethodology