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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/125120

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/125120

Title:	Joint modeling of longitudinal binary data and survival data
Authors:	Hwang, Y.T. 黃佳慧 Huang, C.-H. Wang, C.C. Lin, T.Y. Tseng, Y.K.
Contributors:	統計系
Keywords:	Cox model; generalized linear model; metropolis-hastings algorithm; Monte Carlo EM algorithm; quality of life
Date:	2019-03
Issue Date:	2019-08-13 09:17:53 (UTC+8)
Abstract:	The medical costs in an ageing society substantially increase when the incidences of chronic diseases, disabilities and inability to live independently are high. Healthy lifestyles not only affect elderly individuals but also influence the entire community. When assessing treatment efficacy, survival and quality of life should be considered simultaneously. This paper proposes the joint likelihood approach for modelling survival and longitudinal binary covariates simultaneously. Because some unobservable information is present in the model, the Monte Carlo EM algorithm and Metropolis-Hastings algorithm are used to find the estimators. Monte Carlo simulations are performed to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. Real data are used to demonstrate the feasibility of the proposed model.
Relation:	Journal of Applied Statistics
Data Type:	article
DOI link:	https://doi.org/10.1080/02664763.2019.1590540
DOI:	10.1080/02664763.2019.1590540
Appears in Collections:	[Department of Statistics] Periodical Articles

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