Semiparametric single-index model for estimating optimal individualized treatment strategy

Rui Song; Shikai Luo; Donglin Zeng; Hao Zhang; Wenbin Lu; Zhiguo Li

doi:10.1214/17-EJS1226

Semiparametric single-index model for estimating optimal individualized treatment strategy

Rui Song, Shikai Luo, Donglin Zeng, Hao Zhang, Wenbin Lu, Zhiguo Li

Mathematics

Research output: Contribution to journal › Article

2 Scopus citations

Abstract

Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneous group. In this paper, we propose a new semiparametric additive single-index model for estimating individualized treatment strategy. The model assumes a flexible and nonparametric link function for the interaction between treatment and predictive covariates. We estimate the rule via monotone B-splines and establish the asymptotic properties of the estimators. Both simulations and an real data application demonstrate that the proposed method has a competitive performance.

Original language	English (US)
Pages (from-to)	364-384
Number of pages	21
Journal	Electronic Journal of Statistics
Volume	11
Issue number	1
DOIs	https://doi.org/10.1214/17-EJS1226
State	Published - 2017

Keywords

Personalized medicine
Semiparametric inference
Single index model

ASJC Scopus subject areas

Statistics and Probability

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10.1214/17-EJS1226

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Song, R., Luo, S., Zeng, D., Zhang, H., Lu, W., & Li, Z. (2017). Semiparametric single-index model for estimating optimal individualized treatment strategy. Electronic Journal of Statistics, 11(1), 364-384. https://doi.org/10.1214/17-EJS1226

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