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

机译:多型经常性事件的贝叶斯分析与非参数协变函数的依赖终端

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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.
机译:多型反复事件数据经常发生在纵向研究中。 当终端时间与复发事件时间相关时,可能发生依赖终止。 在本文中,我们同时模拟多型反复事件和依赖终端事件,既由B样条模拟的非参数协变量函数也是如此。 我们开发贝叶斯多变量的脆弱模型,以解释依赖终止和各种类型的复发事件之间的相关性。 广泛的仿真结果表明,误操作非参数的协变量函数可能在参数估计中引入偏差。 该方法的开发是通过并应用于抗高血压和脂质降低治疗的降脂试验组分,以防止心脏病发作试验。

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