Semiparametric single-index model for estimating optimal individualized treatment strategy | Zendy

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

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Semiparametric single-index model for estimating optimal individualized treatment strategy

Author(s) -

Rui Song,

Shikai Luo,

Donglin Zeng,

Hao Helen Zhang,

Wenbin Lu,

Zhiguo Li

Publication year - 2017

Publication title -

electronic journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.482

H-Index - 54

ISSN - 1935-7524

DOI - 10.1214/17-ejs1226

Subject(s) - covariate , mathematics , nonparametric statistics , estimator , single index model , personalized medicine , semiparametric regression , semiparametric model , index (typography) , monotone polygon , mathematical optimization , econometrics , statistics , computer science , bioinformatics , geometry , world wide web , biology

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

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