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Stat Med. 2019 Nov 20;38(26):5146-5159. doi: 10.1002/sim.8357. Epub 2019 Aug 28.

Estimating individual treatment effects by gradient boosting trees.

Author information

1
Center for Spatial Information Science, The University of Tokyo, Kashiwa, Japan.
2
Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.
3
Department of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.

Abstract

The development of molecular diagnostic tools to achieve individualized medicine requires accurate estimation of individual treatment effects (ITEs). Although several effective data analytic strategies have been proposed for this purpose, they have limitations when it comes to flexibly capturing the complex relationships between clinical outcome and possibly high-dimensional covariates. In this article, we propose an effective machine learning method to estimate ITEs using the gradient boosting trees (GBT). GBT is a powerful nonparametric regression tool in machine learning, and its outstanding performance has been widely recognized for various applications. We use GBT to develop an estimation method for the ITE that is formulated under the potential outcome model framework. Our method can flexibly capture the relationship between clinical outcome and possibly high-dimensional covariates, and it would also be useful for identifying subpopulations of patients who would benefit from the treatment. Results of simulation studies and a real-data analysis of a breast cancer clinical study show that the proposed method can precisely estimate ITEs, and these estimates possibly identify the subgroup of patients who can benefit from treatment.

KEYWORDS:

gradient boosting trees; individual treatment effects; potential outcome; precision medicine; subgroup analysis

PMID:
31460679
DOI:
10.1002/sim.8357

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