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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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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 |
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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 |
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