My applied research focuses on simulation models of the progression of multiple chronic complications and comorbidities of diabetes and its precursors. I study the effectiveness and cost-effectiveness of early interventions in the progression of diabetes. My methodological research synthesizes secondary data from complementary studies to model complex processes.
My work falls into three general application areas. I am an applied (accredited) biostatistician with a strong team science motivation and I collaborate with scientists in primarily the biomedical sciences, contributing expertise in experimental design, statistical analysis/modeling, and data visualization. I have held faculty appointments in Schools of Medicine and Nursing, and also worked as a senior scientist in the Human Research Program at the NASA Johnson Space Center. I currently direct an Applied Biostatistics Laboratory and Data Management Core within the UM School of Nursing, and maintain several collaborative research programs within the School, at NASA, and with collaborators elsewhere.
Xingyu Zhang is a Research Assistant Professor at the School of Nursing’s Applied Biostatistics Laboratory. He received his Ph.D. in Biomedical Science concentrated on biostatistics from the University of Auckland in 2016. Prior to joining the ABL, he was a postdoctoral research fellow in epidemiology and biostatistics at Emory University, and also a visiting research scholar in medical informatics at Georgia Institute of Technology. Dr. Zhang’s research focuses on healthcare outcomes with an emphasis on study design and statistical analysis. The methods he applied include multi-level analysis, time series analysis, infection early warning modeling, medical imaging analysis, feature extraction, pattern classification, neural networks, support vector machine, natural language processing, deep learning, survival analysis, meta-analysis, etc.
Dr. Davis is a health services researcher who has additional training in data science. His research focuses on leveraging large sources of data to study important policy-relevant issues. Dr. Davis has made several important contributions to a variety of areas including the identification of dietary sources of arsenic exposure in the US population, studying national use of health services over time for nonspecific back pain, and the development of methods to use social media data to measure social support and public opinion. A specific interest of Dr. Davis is the application of data mining methods to healthcare claims data. Funded by the NIH, his current work is investigating health service substitution for nonspecific back pain by conducting a natural experiment of Medicare patients. He received his Masters in Public Health from Dartmouth Medical School and his PhD in quantitative biomedical sciences from Dartmouth College.