Jinbo Chen, Ph.D.
Professor of Biostatistics in Biostatistics and Epidemiology
Senior Scholar, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine
Senior Scholar, Institute of Biomedical Informatics, University of Pennsylvania Perelman School of Medicine
Associate Director, Penn Center for Precision Medicine
Associate Director, Penn Medicine Biobank
Director, Statistical Center for Translational Research in Medicine (SC-TRM)
Lead Biostatistician, Penn Medicine Biobank (PMBB)
Member, Senior Scientific Advisory Committee, Penn Center for AI and Data Science for Integrated Diagnostics
Member, Executive Faculty Committee, Penn Medicine AI2D center
Department: Biostatistics and Epidemiology
Graduate Group Affiliations
Contact information
University of Pennsylvania Perelman School of Medicine,
Department of Biostatistics, Epidemiology and Informatics
605 Blockley Hall
423 Guardian Drive
Philadelphia, PA 19104
Department of Biostatistics, Epidemiology and Informatics
605 Blockley Hall
423 Guardian Drive
Philadelphia, PA 19104
Office: 215-746-3915
Fax: 215-573-1050
Fax: 215-573-1050
Email:
jinboche@upenn.edu
jinboche@upenn.edu
Publications
Links
Search PubMed for articles
Center for Clinical Epidemiology and Biostatistics Faculty
CCEB > Statistical Genetics and Genomics
Search PubMed for articles
Center for Clinical Epidemiology and Biostatistics Faculty
CCEB > Statistical Genetics and Genomics
Education:
B.S. (Physics)
Beijing Normal University, P.R. China, 1992.
M.S. (Biostatistics)
University of Washington, Seattle, WA, 1999.
Ph.D. (Biostatistics)
University of Washington, Seattle, WA, 2002.
Permanent linkB.S. (Physics)
Beijing Normal University, P.R. China, 1992.
M.S. (Biostatistics)
University of Washington, Seattle, WA, 1999.
Ph.D. (Biostatistics)
University of Washington, Seattle, WA, 2002.
Description of Research Expertise
Dr. Jinbo Chen’s primary areas of research include the development of statistical methods for designing and analyzing two-phase epidemiologic studies, methodological and collaborative work in genetic association studies of complex diseases, and the development and evaluation of risk prediction models. Dr. Chen has published numerous methods papers on haplotype analysis using data from cohort, nested case-control, and matched case-control designs, methods on exploring gene-gene and gene-environment interactions, and efficient methods for screening SNPgenotypes. Her recent work has focused on design and analyses issues arising from studies of complex genetic and environmental effects on pregnancy-related and early-life disorders. Dr. Chen has been collaborating with researchers on dissecting genetic and environmental influences on the risk of biliary tract cancer, childhood leukemia, preterm birth, food allergy, and cardio-metabolic and cardiovascular diseases.
Selected Publications
Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, Benichou J, Gail, MH: Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. Journal of the National Cancer Institute 98(17): 1215-1226, 2006.Wang L, Schnall J, Small A, Hubbard RA, Moore JH, Damrauer SM, Chen J: Case contamination in electronic health records based case-control studies. Biometrics 77(1): 67-77, Mar 2021.
Parikh RB, Zhang Y, Kolla L, Chivers C, Courtright KR, Zhu J, Navathe AS, Chen J: Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic. J Am Med Inform Assoc 30(2): 348-54, Jan 2023.
Hasler JS, Ma Y, Wei YZ, Parikh R, Chen J: A Semiparametric Method for Risk Prediction Using Integrated Electronic Health Record Data. Ann. Appl. Stat 18(4): 3318-3337, Dec 2024.
Dai G, Shao L, Chen J: Moving beyond population variable importance: concept, theory and applications of individual variable importance. Journal of the Royal Statistical Society Series B: Statistical Methodology Page: qkae11, Dec 2024 Notes: https://doi.org/10.1093/jrsssb/qkae115.
Cao Y, Ma W, McCarthy A, Chen J: A constrained maximum likelihood approach to developing well-calibrated models for predicting binary outcomes. Lifetime Data Analysis 30: 624-648, May 2024 Notes: DOI https://doi.org/10.1007/s10985-024-09628-9.