Semiparametric single-index model for estimating optimal individualized treatment strategy

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

Mathematics

Research output: Contribution to journal › Article › peer-review

5 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
Statistics, Probability and Uncertainty

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10.1214/17-EJS1226

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title = "Semiparametric single-index model for estimating optimal individualized treatment strategy",

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.",

keywords = "Personalized medicine, Semiparametric inference, Single index model",

author = "Rui Song and Shikai Luo and Donglin Zeng and Zhang, {Hao Helen} and Wenbin Lu and Zhiguo Li",

note = "Funding Information: Rui Song?s research is partially supported by the NSF grant DMS-1555244 and NCI grant P01 CA142538. Donglin Zeng?s research is partially supported by NIH grants U01-NS082062 and R01GM047845.Wenbin Lu?s research is partially supported by NCI grant P01 CA142538. Hao Helen Zhang?s research is partially supported by the grants NSF DMS-1309507, DMS-1418172 and NSFC 11571009. Zhiguo Li?s research is partially supported by NCI grant P01 CA142538.",

year = "2017",

doi = "10.1214/17-EJS1226",

language = "English (US)",

volume = "11",

pages = "364--384",

journal = "Electronic Journal of Statistics",

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publisher = "Institute of Mathematical Statistics",

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T1 - Semiparametric single-index model for estimating optimal individualized treatment strategy

AU - Song, Rui

AU - Luo, Shikai

AU - Zeng, Donglin

AU - Zhang, Hao Helen

AU - Lu, Wenbin

AU - Li, Zhiguo

N1 - Funding Information: Rui Song?s research is partially supported by the NSF grant DMS-1555244 and NCI grant P01 CA142538. Donglin Zeng?s research is partially supported by NIH grants U01-NS082062 and R01GM047845.Wenbin Lu?s research is partially supported by NCI grant P01 CA142538. Hao Helen Zhang?s research is partially supported by the grants NSF DMS-1309507, DMS-1418172 and NSFC 11571009. Zhiguo Li?s research is partially supported by NCI grant P01 CA142538.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Personalized medicine

KW - Semiparametric inference

KW - Single index model

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