- Ph.D. Biostatistics, University of North Carolina-Chapel Hill
- M.S. Statistics, Chung-Ang University, South Korea
- B.S. Statistics, Chung-Ang University, South Korea
I. Statistical Analysis for Biomarker Discovery and Validation
Biomarkers play an important role in early detection of disease and clinical decision-making process. In particular, recent advances in genomics, molecular biology and imaging technologies promise to seek potential biomarkers that could be non-invasive, cost-effective and accurate. Dr. Chung?s research has been focused on development of statistical methods for evaluating such biomarkers in various studies including:
- cross-sectional study with high-dimensional biomarkers for personalized medicine;
- disease surveillance study with longitudinal biomarkers and/or censored time-to-event outcome;
- two-phase biomarker study in the presence of surrogate biomarkers.
II. Shape-Restricted Hazard Analysis
Isotonic regression is a useful nonparametric technique for fitting a monotone increasing (or decreasing) function. It offers a flexible tool in estimating a monotone regression relationship between response and covariate. His research applies the isotonic regression techniques to survival data under a natural assumption that the hazard function is a monotone function of one of the covariates. Specifically, a monotone function is incorporated to Cox's proportional hazards model, where it captures nonlinear and monotone covariate effects without specifying a baseline hazard function. His current research project includes
- Unimodal (or U-shaped) hazard function where the hazard function is monotone increasing and decreasing over a mode, i.e. estimation of both unimodal hazard and mode are of interest;
- Estimation of monotone hazard function in multiple covariates.
Yunro (Roy) Chung is an assistant professor at the Arizona State University (ASU) with a joint appointment in the College of Health Solutions and Biodesign Center for Personalized Diagnostics (CPD). He is a biostatistician working with an interdisciplinary team at CPD. One of his research interests is to use statistics and machine learning algorithms to discover novel biomarkers using Nucleic Acid-Programmable Protein Array (NAPPA) that lead to better screening and early diagnosis of disease. He is also interested in developing statistical and computational methods for heterogeneous cancer survival data.
He received his doctorate in biostatistics from the University of North Carolina at Chapel Hill and his master's and bachelor's degrees in statistics from Chung-Ang University (South Korea). He provided statistical consulting, power analysis and data analysis for many projects including clinical trials, laboratory experiments and genomics researches. Prior to joining ASU, he was a postdoctoral research fellow at the Fred Hutchinson Cancer Research Center, where he studied an active surveillance study for prostate cancer.