MODELS AND ANALYSIS OF RECURRENT DATA WITH INTERVENTION

这项拟议的研究重点是发展模型， 分析事件观测的统计方法。观察结果 通过以下受试者接受干预计划获得， 延长至下一次复发事件的时间， 兴趣（例如，再次滥用药物，再次心脏病发作， 萧条时期，仅举几例）。我们将开发方法， 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）和确定相对 有效性，以及评估哪些因素（协变量）显著 影响下一个事件的时间。我们的模型和方法更加丰富， 比标准方法更灵活，包括考克斯的比例 基于时间相关协变量的风险模型。特别是我们 模型同时纳入干预效果，削弱（或 强化）事件发生次数的影响，以及 的协变量。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.