Date on Master's Thesis/Doctoral Dissertation
8-2017
Document Type
Doctoral Dissertation
Degree Name
Ph. D.
Department
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
Abstract
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.
Recommended Citation
Wu, You, "Bayesian approach on short time-course data of protein phosphorylation, casual inference for ordinal outcome and causal analysis of dietary and physical activity in T2DM using NHANES data." (2017). Electronic Theses and Dissertations. Paper 2751.
https://doi.org/10.18297/etd/2751
