We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the frailty parameters influencing the response at different times. The order of the autoregression may be assumed unknown and is an object of inference. We consider two alternative approaches to perform model selection under this scenario. Covariates may be easily included in the regression framework and censoring and missing data are easily accounted for. As the proposed methodologies lie within the class of Dirichlet process mixtures, posterior inference can be performed through efficient MCMC algorithms. We illustrate the approach through simulations and medical applications involving recurrent hospitalizations of cancer patients and successive urinary tract infections.

中文翻译：

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用于反复事件推断的贝叶斯自回归脆弱模型。

我们为自发性事件之间的间隔时间提出了自回归贝叶斯半参数模型。目的是双重的：根据重复事件的时间轨迹推断可能时变的协变量对间隔时间的影响以及个体的聚类。间隙时间之间的时间相关性通过针对影响在不同时间响应的脆弱参数的自回归组件的规格来考虑。自回归的顺序可以假定是未知的，并且是推理的对象。我们考虑在这种情况下执行模型选择的两种替代方法。协变量可以很容易地包含在回归框架中，并且很容易考虑到审查和缺失数据。由于建议的方法属于Dirichlet工艺混合物类别，可以通过有效的MCMC算法执行后验推断。我们通过模拟和医学应用说明了该方法，该方法涉及癌症患者的反复住院治疗和连续尿路感染。