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GENERALIZED SPATIOTEMPORAL MODELING AND CAUSAL INFERENCE FOR ASSESSING TREATMENT EFFECTS FOR MULTIPLE GROUPS FOR ORDINAL OUTCOME Soutik Ghosal July 25, 2018 This dissertation consists of three projects and can be categorized in two broad research areas: generalized spatiotemporal modeling and causal inference based on observational data. In the rst project, I introduce a Bayesian hierarchical mixed e ect hurdle model with a nested random e ect structure to model the count for primary care providers and understand their spatial and temporal variation. This study further enables us to identify the health professional shortage areas and the possible impacting factors. In the second project, I have uni ed popular parametric and nonparametric propensity score-based methods to assess the treatment e ect of multiple groups for ordinal outcome. I have conducted di erent simulation scenarios and compared the performance of those methods. In the third project, I have introduced a generalized spatiotemporal model to identify the antibiotic medication overuse in Kentucky. In this project, I used the Medicaid data to understand the spatial and seasonal variation of the antibiotic overuse for children insured by Kentucky Medicaid.

Generalized spatiotemporal modeling and causal inference for assessing treatment effects for multiple groups for ordinal outcome.