Semiparametric single-index model for estimating optimal individualized treatment strategy

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

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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 languageEnglish (US)
Pages (from-to)364-384
Number of pages21
JournalElectronic Journal of Statistics
Issue number1
StatePublished - 2017


  • Personalized medicine
  • Semiparametric inference
  • Single index model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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