MODELS AND ANALYSIS OF RECURRENT DATA WITH INTERVENTION

这项拟议的研究重点是开发模型和 用于分析事件观测的统计方法。观察到的是 通过跟踪受试者设计的干预方案获得 将时间延长到下一次发生 兴趣(例如，重新滥用药物、再次心脏病发作、再次 抑郁症时期，仅举几例)。我们将制定方法，以 预测下一次发生的时间，用于比较各种类型的 干预措施(例如，竞争性治疗)和确定亲属 效率，以及评估哪些因素(协变量)显著 影响下一次活动的时间。我们的模型和方法更加丰富和 比标准方法更灵活，包括考克斯的比例 基于时间依赖协变量的风险模型。尤其是，我们的 模型同时包含干预效应、弱化(或 加强)事件发生次数的影响以及影响 协变量中的。我们将使用这些方法来分析各种数据集 包括来自美国肾脏数据系统的住院数据。

This proposed research focuses on the development of models and statistical methods for analyzing event observations. The observations are obtained by following subjects undergoing intervention programs designed to prolong the time to the next occurrence of the recurrent event of interest (e.g., a return to substance abuse, another heart attack, another period of depression, to name a few). We will develop methods for predicting the time to the next occurrence, for comparing various types of interventions (e.g. competing treatments) and determining the relative efficacies, and for assessing which factors (covariates) significantly affect the time to the next event. Our models and methods are richer and more flexible than standard approaches, including Cox's proportional hazards model based on time-dependent covariates. In particular, our models simultaneously incorporate intervention effects, weakening (or strengthening) effects of the number of event occurrences, and the effects of the covariates. We will use the methods to analyze various data sets including hospitalization data from the US Renal Data System.