Dr. Jing Zhang is an assistant professor at the Department of Epidemiology and Biostatistics of the School of Public Health at University of Maryland. She received a PhD in Biostatistics from the University of Minnesota in 2014. Dr. Zhang conducts research in Bayesian hierarchical methods, missing data analysis, meta-analysis, network meta-analysis, diagnostic tests and clinical trials. Her work has appeared in Clinical Trials, Statistics in Medicine, Statistical Methods in Medical Research, Research Synthesis Methods, and Journal of Staitistical Software.
Associate Editor (2015-Now), Journal of Biopharmaceutical Statistics
Active Research Funding Grants:
2018-2020, NIA, "Effect of Hospital and Community Care Coordination on Health Care Access, Quality and Equity among Individuals with Risk Factors or Diagnosis of ADRD", Co-I.
2018-2020, NIA, "PARAMEDIC-COACHED ED CARE TRANSITIONS TO HELP OLDER ADULTS MAINTAIN THEIR HEALTH", Subcontract Principal Investigator.
2017-2019, NIH National Library of Medicine (NLM), "Joint Meta-Regression Methods Accounting for Postrandomization Variables", Subcontract Principal Investigator.
2016-2021, American Cancer Society, "Integration of cancer health activities into African American churches", Biostatistician.
Ph.D. in Biostatistics, University of Minnesota, 2014
M.S. in Biostatistics, University of Minnesota, 2011
EPIB 652: Categorical Data Analysis
EPIB 300: Biostatistics for Public Health Practice (Syllabus)
EPIB 650: Biostatistics I (Syllabus)
EPIB 786: Capstone Project in Public Health
EPIB 798: Independent Study
EPIB 799: Master's Thesis Research
Travel award -- funded by Google, Inc., Women in Statistics and Data Science, 2018
New Researchers Conference Travel Award, 19th Institute of Mathematical Statistics (IMS) New Researchers Conference, 2017
Junior Researcher's Workshop Travel Award, International Biometric Society/Eastern North American Region Spring Meeting, 2017
Young Investigator Travel Support, G70: A Celebration of Alan Gelfand's 70th Birthday, 2015
Distinguished Student Paper Awards, International Biometric Society/Eastern North American Region Spring Meeting, 2014
Fostering Diversity in Biostatistics Workshop Travel Fund, International Biometric Society/Eastern North American Region Spring Meeting, 2014
Jacob E. Bearman Student Achievement Award, 2014
Young Investigator Award, Statistics in Epidemiology Section, Joint Statistical Meeting, 2013
Travel Award, The 10th International Conference on Health Policy Statistics, 2013
Honorable Mention Recipient of School of Public Health Research Day, 2013
School of Public Health Student Senate Grant, 2013
Dean's PhD scholars Awards, 2012
Graduate School Block Grant Fellowship, 2010
Williams, R., Zhang, J., Woodard, N., Slade, J., Santos, S. L. Z., Holt, C. (2020). "Development and validation of an instrument to assess institutionalization of health promotion in faith-based organizations". Evaluation and Program Planning. Accepted.
Zhang, J., Ko, C., Nie, L., Chen, Y., and Ram, T. (2019). "Bayesian hierarchical methods for meta-analysis combining randomized-controlled and single-arm studies". Statistical Methods in Medical Research. 28(5): 1293-1310.
Yuan, Y., Zhang, J., Chatterjee, S., Yu, S., and Rosale, R. (2019). "A State Transition Model for Mobile Notifications via Survival Analysis". The Twelfth ACM International
Conference on Web Search and Data Mining (WSDM 19), 123-131.
Kyle, J., Lu, Y., Iso-Ahola, S., Zhang, J., Gentili, R.J., Hatfield, B. (2019) "Self-Controlled Practice to Achieve Neuro-Cognitive Engagement: Underlying Brain Processes to Enhance Cognitive-Motor Learning and Performance". Journal of Motor Behavior.
Slopen, N., Zhang, J., Urlacher, S. S., DeSilva, G., Mittal, M. (2018). ``Maternal experiences of intimate partner violence and C-reactive protein levels in young children in Tanzania''. SSM - Population Health. 6: 107-115.
Zhang, J., Chu, H., Hong, H., Virnig, B.A., and Carlin, B.P. (2017). "Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness". Statistical Methods in Medical Research. 26 (5): 2227–2243.
Jaquessa K.J., Gentilia, R.J., Loa, L.C., Oha, H., Zhang, J., Rietschele, J.C., Millerf, M.W., Tana, Y.Y., Hatfielda, B.D. (2017). "Empirical evidence for the relationship between cognitive workload and attentional reserve". International Journal of Psychophysiology. 121: 46-55.
Zhang, J., Yuan, Y., Chu, H. (2016). "The impact of excluding trials from network meta-analyses - an empirical study". PLOS ONE. 11(12): e0165889.
Hong, H., Chu, H., Zhang, J., and Carlin, B.P. (2016). "A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons". Research Synthesis Methods, 7 (1): 6-22.
Hong, H., Chu, H., Zhang, J., and Carlin, B.P. (2016). "Rejoinder to the Discussion of `A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons' by S. Dias and A.E. Ades". Research Synthesis Methods, 7 (1): 29-33. (Related: Dias, S., Ades, A.E. (2016). "Absolute or relative effects? Arm‐based synthesis of trial data". Research Synthesis Methods, 7 (1): 29-33.)
Zhang, J., Fu, H., and Carlin, B.P. (2015). "Detecting outlying trials in network meta-analysis". Statistics in Medicine, 34 (19): 2695-2707.
Zhang, J., Yuan, Y. (2015). "Industry-funded clinical trials: beneficial or harmful?" Clinical Research and Regulatory Affairs, 32 (4): 111-114.
Zhang, J., Carlin, B.P., Neaton, J.D., Soon G.G., Nie L., Kane, R., Virnig B.A., and Chu, H. (2014). “Network meta-analysis of randomized clinical trials: Reporting the proper summaries”. Clinical Trials, 11 (2): 246-262.
Zhang, J., Lin, L. (2014). “Choosing the appropriate statistics”. Network meta-analysis: Evidence synthesis with mixed treatment comparison. Giuseppe Biondi Zoccai (Ed.). New York: Nova Publishers. 139-151.
Zhang, J., Yuan, Y. (2014). "Randomized phase II cancer clinical trials (author: Jung, S. H.)". Invited Book Review. Journal of the American Statistical Association, 109 (508): 1717.
Zhang, J., Cole, S.R., Richardson, D.B., and Chu, H. (2013). “A Bayesian approach to strengthen inference for case-control studies with multiple error-prone exposure assessments". Statistics in Medicine, 32 (25), 4426-4437.