Return to search

Semiparametric single-index model for estimating optimal individualized treatment strategy

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.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/625783
Date13 February 2017
CreatorsSong, Rui, Luo, Shikai, Zeng, Donglin, Zhang, Hao Helen, Lu, Wenbin, Li, Zhiguo
ContributorsUniv Arizona, Dept Math
PublisherINST MATHEMATICAL STATISTICS
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
TypeArticle
RightsThis work is licensed under a Creative Commons Attribution 4.0 International License.
Relationhttp://projecteuclid.org/euclid.ejs/1486976416

Page generated in 0.002 seconds