Statistical Methods for Repeated Events

作为一名统计学家，我开发了新的统计方法来改进研究的设计和分析，在许多项目上合作，并进一步培训HQP。虽然我的提案的主题是由真实的管理数据（即，常规收集的数据主要用于研究以外的目的，我的方法可以应用于许多学科领域，如生物科学和工业，收集大型数据集，研究对象可能会随着时间的推移发生多个事件（甚至可能有多种类型）。我的研究建议的主要目标是：（A）制定一个多变量聚类检测方法，（B）开发马尔可夫和半马尔可夫模型，调整变量删失和粗化，和（C）模拟随时间变化的系数效应的经常性事件数据。统计方法的开发和检查是重要的科学工作，以确保数据分析得出的结论正确，并且不扩散错误的发现。

As a statistician, I develop novel statistical methods to improve the design and analysis of studies, work collaboratively on many projects, and further the training of HQP. While my proposal features topics that are motivated by real administrative data (i.e., routinely collected data primarily assembled for purposes other than research), my methodology could be applied to many subject areas, such as biological sciences and industry, where large datasets are collected and study subjects may have multiple events over time (perhaps even with multiple types). The main objectives of my research proposal are to: (A) formulate a multivariate cluster detection method, (B) develop Markov and semi-Markov models that adjust for variable censoring and coarsening, and (C) simulate recurrent event data with time-varying coefficient effects. The development and examination of statistical methods are important scientific endeavours to ensure that the conclusions reached from the analysis of data are correct and that false findings are not proliferated.