Methods for causal analysis of longitudinal data

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Some of the most fundamental questions in medical and health research are: "Does a particular treatment make sick patients live longer?" or “Does a particular treatment reduce the number of episodes of sickness and individual experiences?”. In some settings these questions can be addressed through the use of a randomized trial however such trials are very expensive and may take many years to complete. An alternative is to try and answer these questions using what is known as observational data. That is, data available from completed studies, registries or databases in which the treatments, outcomes, patient characteristics have been recorded for a group of subjects. ******This research program is focused on answering these types of questions in settings in which the available data has been repeatedly measured over time. The objective is to create a framework for the analysis of data when the outcome of interest is a recurrent event process and an individual's personal characteristics and treatment histories evolve over time. My team and I will also consider settings in which the outcome of interest is survival time and a recurrent event process forms the exposure or treatment of interest. Accounting for the nonrandomized treatment and complex relationship between the variables requires advanced statistical methodology and we do not currently have adequate methods to handle all possible settings.******The methodology to be developed by my team and will not only advance scientific understanding within the field of statistics but can be applied to practical problems anywhere longitudinal data and recurrent events occur. This can range from health related applications (recurrent bouts of disease and/or their association with overall survival), actuarial processes (repeated claims on insurance policies) to industrial applications (recurrent need for repairs affecting the durability or lifetime of a machine). The use of observational data is exciting since it offers the opportunity to benefit from existing data and quickly obtain results. Two doctoral students and five master's students will be trained under this research program helping to meet the growing need for highly trained statisticians and data scientists in Canada. **