Statistical Methods for Repeated Events

重复事件的统计方法

• 批准号: RGPIN-2016-05182
• 负责人: Rosychuk, Rhonda
• 金额: $ 2.4万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635794
• 关键词: Statistical; Methods; Repeated; Events

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.

作为一名统计学家，我开发了新颖的统​​计方法来改进研究的设计和分析，在许多项目上进行协作，并进一步加强 HQP 的培训。虽然我的提案的主题是由真实的管理数据激发的（即，主要为研究以外的目的而收集的常规数据），但我的方法可以应用于许多学科领域，例如生物科学和工业，其中收集了大量数据集，研究对象可能随着时间的推移发生多个事件（甚至可能有多种类型）。我的研究提案的主要目标是：（A）制定多元聚类检测方法，（B）开发针对变量审查和粗化进行调整的马尔可夫和半马尔可夫模型，以及（C）模拟具有时变系数效应的重复事件数据。统计方法的开发和检验是重要的科学努力，以确保通过数据分析得出的结论是正确的，并且错误的结果不会扩散。