Statistical Methods for Analyzing DNA Methylation Data and Subpopulation Analysis of Continuous, Binary and Count Data for Clinical Trials
Year: 2015
Type: dissertation
Abstract: DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has\noutpaced statistical methods for analysis. Novel methodologies are required to accommodate this growing volume of DNA methylation data. In this dissertation, I propose two\nnovel methods to analyze DNA methylation data: (1) a new statistic based on spatial location information of DNA methylation sites to detect differentially methylated regions\nin the genome in case and control studies; and (2) a principal component approach for the detection of unknown substructure in DNA methylation data. For each method, I review existing ones and demonstrate the efficacy of my proposed method using simulation and data application.\n\nMedical research is increasingly focused on personalizing the care of patients. A better understanding of the interaction between treatment and patient specific prognostic factors\nwill enable practitioners to expand the availability of tailored therapies improving patient outcomes. The Subpopulation Treatment Effect Pattern Plot (STEPP) approach\nwas developed to allow researchers to investigate the heterogeneity of treatment effects on survival outcomes across increasing values of a continuously measured covariate, such\nas biomarker measurement. I extend the STEPP approach to continuous, binary and count outcomes which can be easily modeled with generalized linear models (GLM). The statistical significance of any observed heterogeneity of treatment effect is assessed using permutation tests. The method is implemented in the R software package (stepp) and is\navailable in R version 3.1.1. The efficacy of my STEPP extension is demonstrated by using simulation and data application.
Author: Wai‐Ki Yip
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: Wellcome Trust, British Heart Foundation, Diabetes UK, University of Bristol, National Research Foundation, Ministero dell’Istruzione, dell’Università e della Ricerca, National Research Foundation of Korea, National Institutes of Health, Medical Research Council, National Heart, Lung, and Blood Institute, National Cancer Institute
OpenAlex ID: W1859541056