Dr. Yong Chen is an Associate Professor of Biostatistics at the Perelman School of Medicine, University of Pennsylvania (Penn). He is the PI of the PennCIL lab. He is also a Senior Fellow at the Institute of Biomedical Informatics, a Senior Scholar at the Center for Evidence-based Practice at Penn School of Medicine, and a faculty member at the Applied Mathematics & Computational Science Program, Penn Arts & Sciences. His lab collaborates with informaticians, epidemiologists, clinicians and patients to promote health by novel quantitative methods. A key emphasis of his lab is to integrating fundamental principles and wisdoms of statistics into quantitative methods for tackling key challenges in inferring modern biomedical data. He has been working on applied statistics, biomedical informatics, bioinformatics, and evidence-based medicine. His main research interests are bias reduction methods in electronic medical records, dynamic risk prediction, pharmacovigilance, personalized health management strategies using data-driven approaches, integration of heterogeneous data sources, and evidence synthesis. He has served as a principal investigator or co-Investigator of more than 20 projects, funded by NIH, PCORI and AHRQ, and has published over 100 papers at peer reviewed journals.
Dr. Chen received his Bachelor degree in Mathematics in 2003 at the University of Science and Technology of China, Master degree in Pure Mathematics in 2005, and Ph.D. In Biostatistics in 2010 at the Johns Hopkins University under the supervision of Professor Kung-Yee Liang and Professor Charles Rohde. He was the recipient of Sommer Scholar named after the Dean of School of Public Health of the Johns Hopkins University during 2005-2010, and the Margaret Merrell Award for excellence in research, which recognizes outstanding research of a doctoral student at graduation, in 2010. He was also a recipient of the Institute of Mathematical Statistics IMS Travel Award in 2015. He was elected as a member of Society for Research Synthesis Methodology and a member of the International Statistical Institute in 2018.
Duan, R, Cao, M, Ning, Y, Zhu, M, Zhang, B, McDermott, A, Chu, H, Zhou, X, Moore, J, Ibrahim, J, Scharfstein, D, Chen, Y (July, 2019), Global identifiability of latent class models with applications to diagnostic test accuracy studies: a Grobner basis approach, Biometrics (in press).
Chen, Y, Huang, J, Ning, Y, Liang, K-Y and Lindsay, B. (2018) A conditional composite likelihood ratio test with boundary constraints. Biometrika. 105(1): 225-232
Hong, C, Ning, Y, Wang, S,Wu, H, Carroll, RJ and Chen, Y. (2017) PLEMT: A novel pseudolikelihood based EM test for homogeneity in generalized exponential tilt mixture models, Journal of the American Statistical Association 112 (50).
Chen, Y, Ning, J, Ning, Y, Liang, K-Y and Bandeen-Roche, K. (2017) On the pseudolikelihood inference for semiparametric models with boundary problems. Biometrika, 104 (1): 165-179.
Ning, J, Chen, Y, Cai, C, Huang, X and Wang, MC. (2015) On the Dependence Structure of Bivariate Recurrent Event Processes: Inference and Estimation , Biometrika 102(2): 345-358.
Chen, Y and Liang, KY. (2010) On the asymptotic behaviour of the pseudolike ratio test statistic with boundary problemslihood, Biometrika, 97 (3), 603-620.
Li, R, Duan, R, Kember, R, Regeneron Genetic Center, Rader, D, Damrauer, S, Moore, J and Chen, Y. (2019) A regression framework to uncover pleiotropy in large-scale electronic health record data. Journal of the American Medical Informatics Association, ocz084, https://doi.org/10.1093/jamia/ocz084.
Li, R, Chen, Y and Moore, J. (2019). Integration of genetic and clinical information to improve imputation of data missing from electronic health records. Journal of the American Medical Informatics Association.
Tong, J, Huang, J, Wang, X, Moore, JH, Hubbard, R and Chen, Y. (2019) An Augmented Estimation Procedure for EHR-based Association Studies Accounting for Differential Misclassification. Journal of the American Medical Informatics Association (in press).
Huang, J., Duan, R., Hubbard, R., Wu, Y., Moore, JH., Xu, H., and Chen, Y. (2017), A prior knowledge guided integrated likelihood estimation method (PIE) for bias reduction in association studies using electronic health records data, Journal of the American Medical Informatics Association
Methods & Collaboration:
Singh, J, Kallan, M, Chen, Y, Parks, M, Ibrahim, S. (accepted in Aug 2019) Race and discharge disposition after Elective Total Knee Arthroplasty: A risk-adjusted analysis of a large database, JAMA Network Open (in press)
Lake, E, Jordan, J, Duan, R, and Chen, Y. (2019) A Meta-Analysis of the Associations between the Nurse Work Environment in Hospitals and Five Sets of Outcomes , Medical Care 59(5) 353-360
Lin, L, Chu, H, Murad, M, Hong, C, Qu, Z, Cole, S, and Chen, Y. (2018) Empirical Comparison of publication bias tests in meta-analysis , Journal of General Internal Medicine 33(8):1260-1267.
Chen, Y, Wang, J, Chubak, J, Hubbard, R (2018) Inflation of type I error rates due to differential misclassification in EHR-derived outcomes: Empirical illustration using breast cancer recurrence, Pharmacoepidemiology & Drug Safety
Duan, R, Zhang, X., Huang, J., Du, J., Tao, C., and Chen, Y. (2018). On the Evidence Consistency of Pharmacovigilance Outcomes between FAERS and EMR Data for Acute Mania Patients. IEEE International Conference on Health Informatics 2018
Huang, J., Du, J., Duan, R., Zhang, X., Tao, C., Chen, Y. (2018) Characterization of the differential adverse event rates by race/ethnicity groups for HPV vaccine by integrating data from different sources. Frontiers in pharmacology, 9:539
Duan, R, Cao, M, Wu, Y, Huang, J, Denny, J, Xu, H and Chen, Y. (2016) An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies, AMIA annual symposium proceedings, 10:1764-1773
Chahoud, J, Semaan, A, Chen, Y, Cao, M, Rieber, A, Rady, P and Tyring, S. (2016) The Association between Beta-genus Human Papillomavirus and Cutaneous Squamous Cell Carcinoma in Immunocompetent Individuals: a Meta-analysis, JAMA Dermatology, 152(12):1354-1364.
Chen, Y, Cai, Y, Hong, C, and Jackson, D. (2016) Inference for correlated effect sizes using multiple univariate meta-analyses, Statistics in Medicine, 35(9): 1405-1422.
Chen, Y, Hong, C and Riley, R. (2015) An alternative pseudolikelihood method for multivariate random-effects meta-analysis, Statistics in Medicine 34 (3): 361-380.
Chen, Y, Liu, Y, Ning, J, Cormier J and Chu H. (2015) A model for combining case-control and cohort studies in systematic reviews of diagnostic tests, Journal of the Royal Statistical Society: Series C, 64(3): 469-489.