Statistical Methods for the Analysis of Recurrent Events and Event History Data

基于单个事件发生的统计方法不足以全面理解具有复杂事件历史的事件过程的数据产生机制。因此，在许多研究领域中，需要设计用于分析复发事件和其他复杂事件历史的统计方法和模型。忽略事件过程的过去的模型无法全面理解潜在的过程。动态模型是基于强度的随机过程模型，用于研究过去事件发生对过程当前或未来演变的影响。动态模型包括动态协变量，这些协变量本质上是内部的时间依赖性解释变量。动态协变量的固有性质及其与干预措施、治疗效果和异质性的复杂关系，使得存在动态协变量时的建模和推断具有挑战性。另一个重要的问题是在事件历史设置中使用结果依赖性研究设计。这样的设计提供了一种分析事件历史数据的经济有效的方式。然而，在事件历史设置中存在许多方法论问题。

Statistical methods based on the occurrence of a single event are not adequate for a comprehensive understanding of data producing mechanisms of event processes with complex event histories. Therefore, statistical methods and models designed for the analysis of recurrent events and other complex event histories are needed in many fields of study. Models that ignore the past of an event process are incapable of comprehensive understanding of the underlying process. Dynamic models are intensity-based stochastic process models which are used to examine the effect of past event occurrences on the present or future evolution of a process. Dynamic models include dynamic covariates, which are essentially internal time-dependent explanatory variables. The inherent nature of dynamic covariates and their complex relations with interventions, treatment effects and heterogeneity make modelling and inference challenging issues when dynamic covariates are present. Another important issue is the use of the outcome-dependent study designs in event history settings. Such designs provide a cost-effective way to analyze event history data. However, there are many methodological problems in event history settings.