Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests

Statistics in Biosciences - Tập 15 - Trang 545-561 - 2021
Boyi Guo1, Hannah D. Holscher2, Loretta S. Auvil3, Michael E. Welge3, Colleen B. Bushell3, Janet A. Novotny4, David J. Baer4, Nicholas A. Burd5, Naiman A. Khan5, Ruoqing Zhu
1Department of Biostatistics, University of Alabama at Birmingham, Birmingham, USA
2Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Champaign, USA
3National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, USA
4USDA, ARS, Beltsville Human Nutrition Research Center, Beltsville, USA
5Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, USA

Tóm tắt

Estimating the individualized treatment effect has become one of the most popular topics in statistics and machine learning communities in recent years. Most existing methods focus on modeling the heterogeneous treatment effects for univariate outcomes. However, many biomedical studies are interested in studying multiple highly correlated endpoints at the same time. We propose a random forest model that simultaneously estimates individualized treatment effects of multivariate outcomes. We consider a popular study design where covariates and outcomes are measured both before and after the intervention. The proposed model uses oblique splitting rules to partition population space to the neighborhood that experiences distinct treatment effects. An extensive simulation study suggests that the proposed method outperforms existing methods in various nonlinear settings. We further apply the proposed method to two nutrition studies investigating the effects of food consumption on gastrointestinal microbiota composition and clinical biomarkers. The method has been implemented in a freely available R package MOTE.RF at https://github.com/boyiguo1/MOTE.RF .

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