Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions

被引：12

作者：

Lin, Li-An ^{[1
]}

Luo, Sheng ^{[1
]}

Chen, Bingshu E. ^{[2
]}

Davis, Barry R. ^{[1
]}

机构：

[1] Univ Texas Houston, Sch Publ Hlth, Dept Biostat, 1200 Pressler St, Houston, TX 77030 USA

[2] Queens Univ, Dept Publ Hlth Sci, Kingston, ON, Canada

来源：

STATISTICAL METHODS IN MEDICAL RESEARCH | 2017年 / 26卷 / 06期

基金：

加拿大自然科学与工程研究理事会; 美国国家卫生研究院;

关键词：

Recurrent events; joint model; multivariate frailty model; Markov Chain Monte Carlo; hypertension; MULTIVARIATE FRAILTY MODELS; CARDIOVASCULAR-DISEASE; JOINT ANALYSIS; SURVIVAL; REGRESSION; FAILURE; RISK; SPLINES; STROKE;

D O I：

10.1177/0962280215613378

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Multi-type recurrent event data occur frequently in longitudinal studies. Dependent termination may occur when the terminal time is correlated to recurrent event times. In this article, we simultaneously model the multi-type recurrent events and a dependent terminal event, both with nonparametric covariate functions modeled by B-splines. We develop a Bayesian multivariate frailty model to account for the correlation among the dependent termination and various types of recurrent events. Extensive simulation results suggest that misspecifying nonparametric covariate functions may introduce bias in parameter estimation. This method development has been motivated by and applied to the lipid-lowering trial component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial.

引用

页码：2869 / 2884

页数：16

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