Electronic Journal of Statistics

Semiparametric single-index model for estimating optimal individualized treatment strategy

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

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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.

Article information

Electron. J. Statist., Volume 11, Number 1 (2017), 364-384.

Received: February 2016
First available in Project Euclid: 13 February 2017

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation
Secondary: 62G99: None of the above, but in this section

Personalized medicine single index model semiparametric inference

Creative Commons Attribution 4.0 International License.


Song, Rui; Luo, Shikai; Zeng, Donglin; Zhang, Hao Helen; Lu, Wenbin; Li, Zhiguo. Semiparametric single-index model for estimating optimal individualized treatment strategy. Electron. J. Statist. 11 (2017), no. 1, 364--384. doi:10.1214/17-EJS1226. https://projecteuclid.org/euclid.ejs/1486976416

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