In clinical practice, it is often the case where the association between the occurrence of events and time-to-event outcomes is of interest; thus, it can be modeled within the framework of recurrent events. The purpose of our study is to enrich the information available for modeling survival with relevant dynamic features, properly taking into account their possibly time-varying nature, as well as to provide a new setting for quantifying the association between time-varying processes and time-to-event outcomes. We propose an innovative methodology to model information carried out by time-varying processes by means of functional data, modeling each time-varying variable as the compensator of marked point process the recurrent events are supposed to derive from. By means of Functional Principal Component Analysis, a suitable dimensional reduction of these objects is carried out in order to plug them into a Cox-type functional regression model for overall survival. We applied our methodology to data retrieved from the administrative databases of Lombardy Region (Italy), related to patients hospitalized for Heart Failure (HF) between 2000 and 2012. We focused on time-varying processes of HF hospitalizations and multiple drugs consumption and we studied how they influence patients' overall survival. This novel way to account for time-varying variables allowed to model self-exciting behaviors, for which the occurrence of events in the past increases the probability of a new event, and to quantify the effect of personal behaviors and therapeutic patterns on survival, giving new insights into the direction of personalized treatment.

中文翻译：

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事件发生时间过程中重复事件的功能建模

在临床实践中，事件发生与事件发生时间之间的关联常常是令人感兴趣的情况；因此，它可以在经常性事件的框架内建模。我们研究的目的是通过相关的动态特征来丰富可用于建模生存的信息，适当地考虑到它们可能随时间变化的性质，并为量化时变过程与时间之间的关联提供一个新的设置。事件结果。我们提出了一种创新的方法，通过功能数据对时变过程执行的信息进行建模，将每个时变变量建模为重复事件应该源自的标记点过程的补偿器。通过泛函主成分分析，对这些对象进行适当的降维，以便将它们插入 Cox 型函数回归模型中以实现总体生存。我们将我们的方法应用于从伦巴第大区（意大利）的行政数据库中检索到的数据，这些数据与 2000 年至 2012 年间因心力衰竭（HF）住院的患者相关。我们专注于 HF 住院和多种药物消耗的时变过程，我们研究了它们如何影响患者的总生存期。这种解释时变变量的新方法可以模拟自激行为，过去发生的事件会增加新事件发生的概率，并量化个人行为和治疗模式对生存的影响，给出个性化治疗方向的新见解。