Dr. Yu Cheng is Professor and Chair of Statistics at the University of Pittsburgh and a fellow of the American Statistical Association. She is currently working on dynamic treatment regimes, SMART and adaptive designs, causal inference, and win statistics for multiple endpoints. She is the site PI of a PCORI method grant on SMART studies and a co-investigator on projects examining cardiovascular disease and environmental influences on maternal and child health outcomes.
Courses
- STAT 200 Basic Applied Statistics, Fall 2008
- STAT 1000 Applied Statistical Methods, Fall 2010, 2012
- STAT 1631 / 2630 Intermediate Probability, Fall 2011
- STAT 1632 Intermediate Mathematical Statistics, Spring 2008, 2012, 2021
- STAT 2131 Applied Statistical Methods I, Fall 2009, Fall 2015, Fall 2018
- STAT 2132 Applied Statistical Methods II, Spring 2014, 2015, 2022, 2023
- STAT2261 Survival Analysis, Spring 2007, 2009, 2011, 2013, 2015, 2017, 2020, 2021, Fall 2023, 2024
- STAT 2381 Supervised Statistical Consulting, Fall 2020, 2021, 2022
- PhD Statistics, University of Wisconsin-Madison, 2006
- MS Statistics, National University of Singapore, 2001
- BS Statistics, University of Science and Technology of China, 1999
Education & Training
- American Statistical Association (ASA) Fellow, 2024
- ASA Pittsburgh Chapter Statistician of the Year, 2020
Yang, X.*, Cheng, Y., Thall, P., Wahed, A. (2024) “A generalized outcome-adaptive sequential multiple assignment randomized trial design,” Biometrics, 80(3).
Lyu, L.*, Cheng, Y., Wahed, A. (2023) “Imputation-Based Q-Learning for Optimizing Dynamic Treatment Regimes with Right-Censored Survival Outcome,” Biometrics, 79, 3676-3689.
Wang, Z.*, Cheng, Y., Seaberg, E.C., Becker, J.T. (2022) “Quantifying Diagnostic Accuracy Improvement of New Biomarkers for Competing Risk Outcomes,” Biostatistics, 23, 666-682.
Wang, Z.*, Wang, Z.*, Lyu, L.*, Cheng, Y., Seaberg, E.C., Molsberry, S.A., Ragin, A., Becker, J.T. (2023) “Dynamic Impairment Classification Through Arrayed Comparisons," Statistics in Medicine, 42, 52-67.
Li, R., Cheng, Y. and Fine, J. (2014) “Quantile association regression models,” Journal of the American Statistical Association, 109, 230-242.
Current research interests include dynamic treatment regime, complex clinical trial design, causal inference, and multiple endpoints. Her prior research includes disease classification, risk evaluation, quantile association, regression and association analyses of competing risks data, discriminant analysis, and collaborative projects on COVID-19, HIV, smoking cessation, systemic lupus erythematosus, bipolar disorder, depression, eating disorders, and cystic fibrosis.
