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. ******For Objective (A), I want to develop methods that will allow for the detection of geographic aggregations of different types of events. The statistical analysis of geographic data is important in many fields and my planned contributions will enable users to identify aggregations in a timely manner. For example, the results of this research would be important for monitoring any system over geography such as infestations of different insects in a forest or medical claims across a province.******Objective (B) focuses on a different aspect of correlated data where data may be continuously observed over time for a collection of subjects and there may be instances when the exact times of events are unknown or censored (i.e., event is unobserved). I will incorporate the complexity of this coarsened data in Markov and semi-Markov models. The methods developed will impact the analysis of data that are incomplete for some key times and should be less biased than other methods. With my new methods, the conclusions drawn will be valid and the inconvenience of coarsened data will be overcome. These methodological advances are essential for any analysis of coarsened data. ******With advances in computer performance, simulation studies have become a key tool for the assessment of statistical methods. The assessment can only be valid if the generated data generated have the appropriate properties. With complicated data structures, the generation of data may not be trivial. Objective (C) will focus on generating recurrent event data with the additional complexity that the covariates will be time-independent but the estimates of the coefficients for these covariates will be time-dependent. This innovation will be important for my own research investigations and will also assist others in the natural sciences and engineering wanting to perform simulation studies for this type of data. **