Statistical Methods for Repeated Events

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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.