Semiparametric single-index model for estimating optimal individualized treatment strategy
AffiliationUniv Arizona, Dept Math
MetadataShow full item record
PublisherINST MATHEMATICAL STATISTICS
CitationSemiparametric single-index model for estimating optimal individualized treatment strategy 2017, 11 (1):364 Electronic Journal of Statistics
JournalElectronic Journal of Statistics
RightsThis work is licensed under a Creative Commons Attribution 4.0 International License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractDifferent 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.
NoteOpen Access Journal.
VersionFinal published version
SponsorsNSF [DMS-1555244, DMS-1309507, DMS-1418172]; NCI [P01 CA142538]; NIH [U01-NS082062, R01GM047845]; NSFC