Joint Analysis of Current Count and Current Status Data

doi:10.1016/j.jmva.2015.08.015

淡江大學機構典藏 > 理學院 > 應用數學與數據科學學系 > 期刊論文 > Item 987654321/109390

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/109390

Title:	Joint Analysis of Current Count and Current Status Data
Authors:	Wena, Chi-Chung;Chen, Yi-Hau
Keywords:	Correlated data;Frailty model;Panel count;Self-consistency;Semiparametric maximum likelihood
Date:	2016
Issue Date:	2017-01-18 02:10:32 (UTC+8)
Publisher:	Academic Press
Abstract:	We consider joint analysis of event times to a recurrent and a non-recurrent event, with the event time data subject to type I interval censoring. The motivation arises from a survey study, which collected current count data for time to occurrences of fracture (recurrent event), and current status (i.e., type I interval censored) data for time to osteoporosis (non-recurrent event). The aim of the study is to examine risk factors for, and levels of association between, the recurrent and non-recurrent events. We propose a joint analysis of current count and current status data based on a joint modeling for recurrent and non-recurrent events. In the proposed framework, a non-homogeneous Poisson process is assumed for the recurrent event, a proportional hazards model is assumed for failure time of the non-recurrent event, and the two event time processes share a common gamma frailty. A semiparametric maximum likelihood estimator, together with a stable computation algorithm, is developed for the joint model. The parametric (covariate effects and frailty) and nonparametric (baseline mean and cumulative hazard functions) components of the estimator are consistent at rates of square root and cubic root of the sample size, respectively. The asymptotic normality for the parametric component of the estimator is established. The application to the survey data mentioned above shows that, female is a common strong risk factor for both fracture and osteoporosis, and times to fracture and osteoporosis are highly associated.
Relation:	Journal of Multivariate Analysis 143, p.153-164
DOI:	10.1016/j.jmva.2015.08.015
Appears in Collections:	[應用數學與數據科學學系] 期刊論文

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