Education and Training:
- Ph.D., Statistics, Iowa State University, 2002
- M.S., Statistics, Iowa State University, 1998
- B.S., Mathematics, Peking University, 1996
Appointments and Positions:
- Associate Professor, Department of Biostatistics and Epidemioloyg, OUHSC, 2011-present
- Associate Professor, Department of Clinical Sciences and Simmons Cancer Center, University of Texas Southwestern Medical Center at Dallas, 2010-2011
- Sr. Research Scientist, Eli Lilly and Company, 2006-2010
- Research Scientist, Eli Lilly and Company, 2004-2006
- Sr. Statistician, Eli Lilly and Company, 2002-2004
Complete List of Publications:
www.ncbi.nlm.nih.gov/pubmed?term=yan d zhao&cmd=DetailsSearch
Most Recent 5 Publications:
- Zhao YD, Rahardja D, Wang D, Shen H (2014). Testing for homogeneous stratum effects in stratified paired binary data. Journal of Biopharmaceutical Statistics, 24(3), 600–607
- Chang K, Li R, Papari-Zareei M, Watumull L, Zhao YD, Auchus R, and Sharifi N (2011). Dihydrotestosterone synthesis bypasses testosterone synthesis to drive castration-resistant prostate cancer. Proceedings of National Academy of Science, 108(33), 13728–13733.
- Matthiesen C, Thompson S, Ahmad S, Syzek E, Zhao YD, Herman T, Bogardus C (2013). A comparison of the 6th and 7th editions of the AJCC TNM systems for T-Classification and predicting the outcomes of advanced (T2-T4) non-melanoma skin cancers treated with radiotherapy. Journal of Radiation Oncology, 2(1), 79–85.
- Daniels, M.J. and Zhao, Y.D. (2003). Modeling the random effects covariance matrix in longitudinal data. Statistics in Medicine, 22, 1631–1647.
- Zhao, Y.D. (2006). Sample size estimation for the van Elteren test – a stratified Wilcoxon-Mann-Whitney test. Statistics in Medicine, 25, 2675–2687.
- Multiple testing and adaptive designs in clinical trials
- Sample size and power calculations for nonparametric tests
- Design, monitoring, analysis, and reporting of clinical trial data
Current Major Research Projects:
- Design and analysis issues in misclassified data
- Adaptive designs for clinical trials with sensitive subgroups of patients
- Collaborative projects in biology and medicine