Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event.

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Xinyuan Tian, Maria Ciarleglio, Jiachen Cai, Erich J Greene, Denise Esserman, Fan Li, Yize Zhao
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引用次数: 0

Abstract

Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommodate this complex data structure. To achieve robustness, we consider the Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific shared frailty distribution, along with an efficient sampling algorithm for posterior inference. Our method is applied to a recent cluster randomized trial on fall injury prevention.

针对零膨胀和终结事件的群集重复事件的贝叶斯半参数推断。
反复事件在临床研究中很常见,而且往往会出现终末事件。在实用性试验中,参与者往往嵌套在临床中,可能易受反复事件的影响,也可能在结构上不受其影响。我们开发了一种贝叶斯共享随机效应模型,以适应这种复杂的数据结构。为了实现稳健性,我们考虑用 Dirichlet 过程来模拟生存过程加速失败时间模型的残差以及特定群组的共享虚弱分布,并采用高效的抽样算法进行后验推断。我们的方法被应用于最近一项关于预防跌倒伤害的分组随机试验中。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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