Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Bioinformatics and Biostatistics

Degree Program

Biostatistics, PhD

Committee Chair

Kong, Maiying

Committee Co-Chair (if applicable)

Datta, Susmita

Committee Member

Gaskins, Jeremy

Committee Member

Zheng, Qi

Committee Member

Little, Bert

Author's Keywords

bayesian; protein phosphorylation; causal inference; ordinal outcome; path analysis; T2DM


This dissertation contains three different projects in proteomics and causal inferences. In the first project, I apply a Bayesian hierarchical model to assess the stability of phosphorylated proteins under short-time cold ischemia. This study provides inference on the stability of these phosphorylated proteins, which is valuable when using these proteins as biomarkers for a disease. in the second project, I perform a comparative study of different confounding-adjusted to estimate the treatment effect when the outcome variable is ordinal using observational data. The adjusted U-statistics method is compared with other methods such as ordinal logistic regression, propensity score based stratification and matching. In the third project, I perform a causal analysis of the combination of dietary information and physical activity in type 2 diabetes across different ethnic groups: White, African American and Mexican American. Such information may contribute to a better understanding of type 2 diabetes variation between ethnic groups, and a better understanding of type 2 diabetes among different ethnic groups and between female and male.

Included in

Biostatistics Commons