Jong H. Jeong, PhD

My main research area has been time-to-event data analysis and clinical trials. In time-to-event data analysis, I have worked on frailty modeling, efficiency of survival probability estimates from the proportional hazards model, weighted log-rank test, competing risks, quantile residual lifetime/life lost, and likelihood theory such as empirical likelihood and hierarchical likelihood.

In clinical trials, I have been involved in several influential phase III breast cancer clinical trials, including work on developing a prediction model based on microarray data collected from different platforms, through the National Surgical Adjuvant Breast and Bowel Project (NSABP) as a co-investigator. My current active collaboration is focused on pain medicine, developing prediction models and facilitating clinical trials. I also serve as a senior faculty member in the CTSI Biostatistics, Epidemiology, and Research Design (BERD) Core at University of Pittsburgh. 

My recent method and collaborative research efforts have evolved toward the areas of machine learning and causal inference in survival analysis. Specifically, a deep learning algorithm has been developed for the quantile regression for censored time-to-event data, and the random forest algorithm has been applied to predict an optimal combination of therapies for patients in pain.

I graduated 6 MS and 11 PhD students, and am currently advising 1 PhD student. I have been teaching Statistical Theory, Survival Analysis, and Mixed Models, all at both MS and PhD levels. I currently serve on the editorial board for Lifetime Data Analysis, the only journal dedicated to time-to-event data analysis. I am a Fellow of the American Statistical Association (ASA), an elected member of the International Statistical Institute (ISI), and an elected member of Omicron of the Delta Omega Society (Honorary Public Health Society).

1996 | University of Rochester, Rochester, NY | PhD in Statistics

BIOST 2043: Introduction to Statistical Theory I
BIOST 2044: Introduction to Statistical Theory II
BIOST 2049: Applied Regression Analysis
BIOST 2051: Statistical Estimation Theory
BIOST 2054/STAT 2261: Survival Analysis
BIOST 2066: Applied Survival Analysis

BIOST 2086: Mixed Models

Books

1. Ha I., Jeong, J., Lee, Y. (2017). Statistical Modelling of Survival Data with Random Effects: H-likelihood Approach. Springer.
2. Jeong, J. (2014). Statistical Inference on Residual Life. Springer: New York.

Peer-reviewed papers

1. Jeong, J. and Oakes, D. (2003). On the asymptotic relative efficiency of estimates from Cox’s model. Sankhya, 65,411-421.
2. Jeong, J. and Fine, J. (2006). Direct parametric inference for cumulative
   incidence function. Journal of Royal Statistical Society-Series C (Applied Statistics) 55, 187-200.
3. Jeong, J. (2006). A new parametric distribution for modeling cumulative
   incidence function: Application to breast cancer data. Journal of Royal Statistical Society-Series A (Statistics in Society). 169, 289-303.
4. Jeong, J. and Jung, S. (2006). Rank tests for clustered survival data when dependent subunits are randomized. Statistics in Medicine. 25, 361-373.
5. Jeong, J. and Fine, J. (2007). Parametric regression on cumulative incidence function. Biostatistics 8, 184-196.
6. Jeong, J., Jung, S, and Joseph Costantino. (2008). Nonparametric inference on  median residual lifetimes in breast cancer patients. Biometrics 64, 157-163.
7. Jeong, J. and Fine, J.P. (2009). A note on quantile residual life under competing risks. Biometrika 96, 237-242.
8. Zhou, M. and Jeong, J. (2011). Empirical likelihood ratio test for median and mean residual lifetime. Statistics in Medicine 30, 152-159.
9. Tang, S. and Jeong, J. (2012). Median tests for censored survival data; a contingency table approach. Biometrics 68, 983-989.
10. Park, T., Jeong, J., and Lee, J. (2012). Nonparametric Bayesian inference on quantile residual life function. Statistics in Medicine 31, 1972–1985.
11. Ha, I., Christian, N., Jeong, J., Park, J., and Lee, Y. (2014). Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties. Statistical Methods in Medical Research, DOI: 10.1177/0962280214526193.
12. Balmert, L. and Jeong, J. (2016). Nonparametric inference on quantile lost lifespan. Biometrics, 73(1), 252-259.
13. Jeong, J. (2018). Domain of inverse double arcsine transformation. arXiv:1811.07827.
14. Jia, Y. and Jeong, J. (2021). Deep learning for quantile regression: DeepQuantreg. Computational Statistics and Data Analysis. https://doi.org/10.1016/j.csda.2021.107323. 
15. Zhang, D. and Jeong, J. (2021). Inference on win ratio for cluster-randomized semi-competing risk data. Japanese Journal of Statistics and Data Science. In press.
16. Fisher, B, Jeong, J., Anderson, S. Bryant, J., Fisher, E., and Wolmark Norman. (2002). Twenty-five year findings from a randomized clinical trial comparing radical mastectomy with total mastectomy and with total mastectomy followed by radiation therapy. New England Journal of Medicine, vol. 347, 8, 567-575.
17. Fisher, B., Jeong,J., Bryant, J., Mamounas, E.P., Dignam, J., and Wolmark, N. (2004) Treatment of lymph node-negative, estrogen receptor-positive breast cancer: long-term findings from National Surgical Adjuvant Breast and Bowel Project clinical trials. Lancet, 364, 858-868.
18. Mell, L. and Jeong, J. (2010). Pitfalls of using composite primary end points in the presence of competing risks. Journal of Clinical Oncology, 28:4297-4299.
19. Pogue-Geile, K.L., Kim, C., Jeong, J. et al. (2013). Predicting degree of benefit from adjuvant trastuzumab in NSABP Trial B-31. Journal of the National Cancer Institute, 105, 1782-1788.

20. Mamounas, E.P., Bandos H., Lembersky B.C., Jeong J. et al. (2019). Use of letrozole after aromatase inhibitor-based therapy in postmenopausal breast cancer (NRG Oncology/NSABP B-42): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncology, 20: 88-99.

21. Gillman A., Zhang D., Jarquin S., Karp J.F., Jeong J, Wasan AD. (2020). Comparative effectiveness of embedded mental health services in pain management clinics versus standard care. Pain Medicine, 21(5):978-991.