TY - JOUR
T1 - Semiparametric regression analysis of multivariate doubly censored data
AU - Li, Shuwei
AU - Hu, Tao
AU - Tong, Tiejun
AU - Sun, Jianguo
N1 - Funding Information:
This work was partly supported by the National Nature Science Foundation of China grant nos. 11671274, 11731011 and 11671168, the Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan grant CIT & TCD 201804078, the Capacity Building for Sci-Tech Innovation-Fundamental Scientific Research Funds grant 025185305000/204, the Youth Innovative Research Team of Capital Normal University, and the Science and Technology Developing Plan of Jilin Province grant 20170101061 J.C.
PY - 2020/10
Y1 - 2020/10
N2 - This article discusses regression analysis of multivariate doubly censored data with a wide class of flexible semiparametric transformation frailty models. The proposed models include many commonly used regression models as special cases such as the proportional hazards and proportional odds frailty models. For inference, we propose a nonparametric maximum likelihood estimation method and develop a new expectation–maximization algorithm for its implementation. The proposed estimators of the finite-dimensional parameters are shown to be consistent, asymptotically normal and semiparametrically efficient. We also conduct a simulation study to assess the finite sample performance of the developed estimation method, and the proposed methodology is applied to a set of real data arising from an AIDS study.
AB - This article discusses regression analysis of multivariate doubly censored data with a wide class of flexible semiparametric transformation frailty models. The proposed models include many commonly used regression models as special cases such as the proportional hazards and proportional odds frailty models. For inference, we propose a nonparametric maximum likelihood estimation method and develop a new expectation–maximization algorithm for its implementation. The proposed estimators of the finite-dimensional parameters are shown to be consistent, asymptotically normal and semiparametrically efficient. We also conduct a simulation study to assess the finite sample performance of the developed estimation method, and the proposed methodology is applied to a set of real data arising from an AIDS study.
KW - expectation–maximization algorithm
KW - frailty model
KW - Maximum likelihood estimation
KW - Multivariate doubly censored data
KW - semiparametric efficiency
UR - http://www.scopus.com/inward/record.url?scp=85069829389&partnerID=8YFLogxK
U2 - 10.1177/1471082X19859949
DO - 10.1177/1471082X19859949
M3 - Journal article
AN - SCOPUS:85069829389
SN - 1471-082X
VL - 20
SP - 502
EP - 526
JO - Statistical Modelling
JF - Statistical Modelling
IS - 5
ER -
