Semiparametric regression analysis of multivariate doubly censored data

• Published in: Statistical modelling Vol. 20; no. 5; pp. 502 - 526
• Main Authors: Li, Shuwei, Hu, Tao, Tong, Tiejun, Sun, Jianguo
• Format: Journal Article
• Language: English
• Published: New Delhi, India SAGE Publications 01.10.2020
• Subjects: Maximum likelihood estimation; Multivariate doubly censored data; frailty model; expectation–maximization algorithm; semiparametric efficiency
• ISSN: 1471-082X; 1477-0342
• DOI: 10.1177/1471082X19859949

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.