TY - JOUR
T1 - Estimation for optimal treatment regimes with survival data under semiparametric model
AU - Fang, Yuexin
AU - Zhou, Yong
N1 - Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.
PY - 2020
Y1 - 2020
N2 - In this paper, we consider a semiparametric model to find the optimal treatment regimes. A-learning type equation method is proposed to construct a doubly robust estimating equation for the parameters of interest in the optimal treatment. To overcome bias from the censoring time, we consider the inverse probability censoring weighting method in estimating equation. The resulting estimator is shown to be consistent and asymptotic normal when either the baseline effect model for covariates or the propensity score is correctly specified. Also, numerical simulations and an application with real data illustrate the proposed method.
AB - In this paper, we consider a semiparametric model to find the optimal treatment regimes. A-learning type equation method is proposed to construct a doubly robust estimating equation for the parameters of interest in the optimal treatment. To overcome bias from the censoring time, we consider the inverse probability censoring weighting method in estimating equation. The resulting estimator is shown to be consistent and asymptotic normal when either the baseline effect model for covariates or the propensity score is correctly specified. Also, numerical simulations and an application with real data illustrate the proposed method.
KW - A-learning estimating equations
KW - Semiparametric model
KW - doubly robust estimator
KW - inverse probability weighting
UR - https://www.scopus.com/pages/publications/85089864109
U2 - 10.1080/03610926.2020.1808686
DO - 10.1080/03610926.2020.1808686
M3 - 文献综述
AN - SCOPUS:85089864109
SN - 0361-0926
VL - 51
SP - 883
EP - 894
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 4
ER -