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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/625783 |
| Date | 13 February 2017 |
| Creators | Song, Rui, Luo, Shikai, Zeng, Donglin, Zhang, Hao Helen, Lu, Wenbin, Li, Zhiguo |
| Contributors | Univ Arizona, Dept Math |
| Publisher | INST MATHEMATICAL STATISTICS |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | This work is licensed under a Creative Commons Attribution 4.0 International License. |
| Relation | http://projecteuclid.org/euclid.ejs/1486976416 |
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