具有非参数协变量函数的多类型复发事件和相关终止的贝叶斯分析。
Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions.
作者信息
Lin Li-An, Luo Sheng, Chen Bingshu E, Davis Barry R
机构信息
1 Department of Biostatistics, The University of Texas School of Public Health, USA.
2 Department of Public Health Sciences, Queen's University, Canada.
出版信息
Stat Methods Med Res. 2017 Dec;26(6):2869-2884. doi: 10.1177/0962280215613378. Epub 2015 Nov 6.
Abstract
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.
摘要
多类型复发事件数据在纵向研究中频繁出现。当终止时间与复发事件时间相关时，可能会出现相依性终止。在本文中，我们同时对多类型复发事件和一个相依性终止事件进行建模，两者均采用由B样条建模的非参数协变量函数。我们开发了一个贝叶斯多变量脆弱模型，以考虑相依性终止与各类复发事件之间的相关性。大量模拟结果表明，非参数协变量函数设定错误可能会在参数估计中引入偏差。该方法的开发是受抗高血压和降脂治疗预防心脏病发作试验的降脂试验部分所推动，并已应用于该部分试验。
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