Date of Award
2023
Document Type
Open Access Dissertation
Degree Name
Doctor of Philosophy in Statistics (PhD)
Administrative Home Department
Department of Mathematical Sciences
Advisor 1
Qiuying Sha
Committee Member 1
Kui Zhang
Committee Member 2
Xiao Zhang
Committee Member 3
Laura E. Brown
Abstract
This dissertation includes three Chapters. In Chapter One, we develop a computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. In this paper, based on the existing CLC method and ACAT strategy, we develop the ceCLC method to test association between multiple phenotypes and a genetic variant. In Chapter Two, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. In Chapter Three, we investigate the relationship between health service costs (medical cost, pharmacy cost, and total cost) and diabetic medication adherence for patients with diabetes in the UPHP population. This finding indicates that despite higher pharmacy spending, increasing medication adherence can significantly reduce the medical cost. Moreover, medication adherence based on different medicines has different effects on total healthcare cost and medical cost.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Wang, Meida, "STATISTICAL METHODS FOR GWAS AND THE IMPACT OF DIABETIC MEDICATION ADHERENCE ON HEALTHCARE COSTS", Open Access Dissertation, Michigan Technological University, 2023.
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