Date on Master's Thesis/Doctoral Dissertation

8-2018

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Bioinformatics and Biostatistics

Degree Program

Biostatistics, PhD

Committee Chair

Kong, Maiying

Committee Member

Gaskins, Jeremy

Committee Member

Kulasekera, K.B.

Committee Member

Mitra, Ritendranath

Committee Member

Ugiliweneza, Beatrice

Author's Keywords

Hierarchical hurdle model; health professional shortage; superiority score; ordinal outcome; antibiotic overuse; generalized spatiotemporal model

Abstract

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 first project, I introduce a Bayesian hierarchical mixed effect hurdle model with a nested random effect 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 unified popular parametric and nonparametric propensity score-based methods to assess the treatment effect of multiple groups for ordinal outcome. I have conducted different 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.

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