Jing Zhang
Assistant Professor, Epidemiology and Biostatistics
Other Affiliations: Public Health Science
Campus: UMD | Building: School of Public Health Building | Room: 2234Q
Phone: (301)405-3085 |
CV / Resume
PDF icon JZ_CV.pdf

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. 

Editorial Boards:

Associate Editor (2015-Now), Journal of Biopharmaceutical Statistics

Education and Training

Ph.D. in Biostatistics, University of Minnesota, 2014

M.S. in Biostatistics, University of Minnesota, 2011


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


Honors and Awards

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



Lin, L., Zhang, J., Hodges, J., and Chu, H. (2017). "Performing arm-based network meta-analysis in R with the pcnetmeta package". Journal of Statistical Software. 80 (5): doi: 10.18637/jss.v080.i05. 

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. 

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., Chu, H., Hong, H., Virnig, B.A., and Carlin, B.P. (2015). "Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness". Accepted Statistical Methods in Medical Research.

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.