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 | |
| State | Published - 2017 |
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Keywords
- Personalized medicine
- Semiparametric inference
- Single index model
ASJC Scopus subject areas
- Statistics and Probability
Cite this
Semiparametric single-index model for estimating optimal individualized treatment strategy. / Song, Rui; Luo, Shikai; Zeng, Donglin; Zhang, Hao; Lu, Wenbin; Li, Zhiguo.
In: Electronic Journal of Statistics, Vol. 11, No. 1, 2017, p. 364-384.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Semiparametric single-index model for estimating optimal individualized treatment strategy
AU - Song, Rui
AU - Luo, Shikai
AU - Zeng, Donglin
AU - Zhang, Hao
AU - Lu, Wenbin
AU - Li, Zhiguo
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
UR - http://www.scopus.com/inward/record.url?scp=85012964483&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85012964483&partnerID=8YFLogxK
U2 - 10.1214/17-EJS1226
DO - 10.1214/17-EJS1226
M3 - Article
AN - SCOPUS:85012964483
VL - 11
SP - 364
EP - 384
JO - Electronic Journal of Statistics
JF - Electronic Journal of Statistics
SN - 1935-7524
IS - 1
ER -