Epidemiology & Public Health
Education and Training
Postdoctoral researcher, University of Pennsylvania, 2021.
Ph.D. in Biostatistics, Pennsylvania State University, 2020.
Dr. Chixiang Chen is an Assistant Professor in Biostatistics who joined the Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine since 2021. Dr. Chen has worked across both theoretical and applied areas of statistics, also has extensive interdisciplinary collaborations including clinical trial design and analysis in neuroscience, medicare claim data, electronical health records, imaging data analysis, oncology research, among others.
Dr. Chen has a very solid background in theoretical statistics and a profound understanding of how to use and implement powerful statistics for clinical and biomedical studies. One of his current research interests is proposing a novel and general computational framework to integrate information from external sources to improve the primary analysis. The newly proposed framework makes multiple information borrowing feasible with stable and scalable computation load. Morevoer, Dr.Chen is highly interested in developing distributed algorithms for analyzing large-scale data (federated data or centralized data), statistical methods to improve the Machine Learning (ML) and causal inference in clinical research, with broad applications in different data structure (bulk RNA-seq, single-cell RNA-seq, etc.).
Please find details from my Personal Website: https://sites.google.com/view/chixiangchen/home
Statistical Method: Data/Information Integration; Causal inference and machine learning; Distributed learning in a large-scale data; Cell Type Deconvolution and single cell data analysis. Biomedical Studies: Clinical studies in neuroscience; Aging; Alzheimer's disease and Rehabilitation of older adults; Brain aging; Single-cell transcriptomics
Selected Publications in Biostatistics and Bioinformatics (* corresponding author):
- Chen, C.*, Wang, M., and Chen, S. (2023). An efficient data integration scheme to synthesize information from multiple secondary outcomes to the main data analysis. Biometrics. Accepted. https://doi.org/10.1111/biom.13858
- Chen, C., Shen, B., Zhang, L., Yu, T., Wang, M., Wu, R. (2023). DRDNetPro: A cartographic tool to predict Disease Risk-associated pseudo-Dynamic Networks from tissue specific gene expression. Bio-protocol. 13(1), e4583.
- Chen, C.*, Leung, Y., Ionita, M., Wang, L.-S., and Li, M. (2022). Omnibus and Robust Deconvolution Scheme of Bulk RNA Sequencing Data via Integrating Multiple Single-cell reference sets and Prior Biological Knowledge. Bioinformatics. 38(19), 4530–4536.
Chen, C.*, Han, P., and He, F. (2022). Improving main analysis by borrowing information from auxiliary data. Statistics in Medicine. 41(3):567-579.
- Chen, C., Jiang, L., Shen, B., Wang, M., Griffin, C., Chinchilli, V., and Wu, R. (2021). A computational atlas of tissue-specific regulatory networks. Frontiers in Systems Biology. 1:764161. doi:10.3389/fsysb.2021.764161.
- Chen, C., Shen, B., Liu, A., Wu, R. and Wang, M. (2021). A multiple robust propensity score method for longitudinal analysis with intermittent missing data. Biometrics, 77(2), 519-532.
Selected Publications in interdisciplinary science:
- Chryssikos, T., Stokum, J. A., Ahmed, A. K., Chen, C., Wessell, A., Cannarsa, G., ... & Aarabi, B. (2023). Surgical Decompression of Traumatic Cervical Spinal Cord Injury: A Pilot Study Comparing Real-Time Intraoperative Ultrasound After Laminectomy With Postoperative MRI and CT Myelography. Neurosurgery, 92(2), 353-362.
- Aarabi, B.,Chen, C., Simard, J. M., Chryssikos, T., Stokum, J. A., Sansur, C. A., ... & Schwartzbauer, G. T. (2022). Proposal of a Management Algorithm to Predict the Need for Expansion Duraplasty in American Spinal Injury Association Impairment Scale Grades A–C Traumatic Cervical Spinal Cord Injury Patients. Journal of neurotrauma, 39(23-24), 1716-1726.
- Carney, C. P., Kapur, A., Anastasiadis, P., Ritzel, R. M., Chen, C., Woodworth, G. F., ... & Kim, A. J. (2022). Fn14-Directed DART Nanoparticles Selectively Target Neoplastic Cells in Preclinical Models of Triple-Negative Breast Cancer Brain Metastasis. Molecular Pharmaceutics. Accepted.
- Mo, C., Wang, J., Ye, Z., Ke, H., Liu, S., Hatch, K., Gao, S., Magidson, J., Chen, C., ... & Chen, S. (2022). Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank. Addiction. Accepted.
- Ye, Z., Mo, C., Ke, H., Yan, Q., Chen, C., Kochunov, P., ... & Ma, T. (2022). Meta-analysis of transcriptome-wide association studies across 13 brain tissues identified novel clusters of genes associated with nicotine addiction. Genes, 13(1), 37.
- Bluethmann, S. M., Wang, M., Wasserman, E., Chen, C., Zaorsky, N. G., Hohl, R. J., & McDonald, A. C. (2020). Prostate cancer in Pennsylvania: The role of older age at diagnosis, aggressiveness, and environmental risk factors on treatment and mortality using data from the Pennsylvania Cancer Registry. Cancer medicine, 9(10), 3623-3633.
- Gardner, A. W., Montgomery, P. S., Wang, M., Chen, C., Kuroki, M., & Kim, D. J. K. (2019). Vascular inflammation, calf muscle oxygen saturation, and blood glucose are associated with exercise pressor response in symptomatic peripheral artery disease. Angiology, 70(8), 747-755.
Dr. Chen has broad interests in both theoretical methodology development and statistical application in multidisciplinary areas. Recently, he is especially interested in developing robust statistical frameworks to integrate information from multiple data sources, with applications in both clinical and genomics studies. Many ongoing works involve survival analysis, causal inference, single-cell data analysis, among others, under the umbrella of the proposed framework.
Another aspect he is working on is to develop a statistical framework to recover dynamic networks from static-state data. The collection of temporal or perturbed data is a prerequisite for reconstructing dynamic networks. However, these types of data are seldom available for genomic studies in medicine, significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases. One proposed framework from Dr. Chen's team makes the reconstruction possible from steady-state data by introducing an agent and incorporating a varying coefficient model with ordinal differential equations. Multiple networks can be inferred corresponding to covariate effects that are linked to known or latent agents, such as disease risk.
In addition, Dr. Chen has experience on model selection, robust longitudinal data analysis, missing data, and other statistical topics with applications in biomedical studies. Besides statistical methodology development, Dr. Chen has worked on multiple projects with collaborators in various disciplines. It ranges from angiology, biochemistry, neuroscience, among others. From past five years with expertise in biostatistics, he denoted himself to data management and preprocessing, modeling and statistical analysis, result evaluation and interpretation as well as intellectual contributions to the work and any publications that result from it.
Dean's Award for Scholarly Achievement, College of Medicine, Penn State Univ. May.2021.
Alumni Society Award, College of Medicine, Penn State Univ. Sep.2019.
JSM Student Paper Award, ASA Nonparametric Section. Jul.2019.
Scholarship Award, the 24th Summer Institute in Statistical Genetics, Univ. of Washington. Jul.2019.
Biopharm-Deming Student Scholar Award, ASA Biopharmaceutical Section and the 74th Annual Deming Conference on Applied Statistics. Dec.2018.
Student Paper Award, ICSA Applied Statistics Symposium. Jun.2018.
Funded (07/2022-06/2027) (Co-Inv; PI-George Uhl) DA056039 "PTPRD phosphatase inhibitors for stimulant use disorders " U01, NIH
Funded (07/2022-06/2024) (Co-Inv; PI-Daniel Harrison) MS210103 "Adaptive Optics Retinal Imaging in Multiple Sclerosis" CDMRP, DOD
Funded (06/2022-05/2025) (Co-Inv; PI-Daniel Harrison) RG-2110-38460 "Development of a Convolutional Neural Network for MRI Prediction of Progression and Treatment Response in Progressive Forms of Multiple Sclerosis" National Multiple Sclerosis Society
To be funded (Co-Inv; PI-Graeme Woodworth) CA269995 "Nanotherapeutic enhancement of interstitial thermal therapy for glioblastoma " R01, NIH
Editorial Assistant, ICSA Bulletin 2021 Issue. Dec.2020-Jan.2023
Session Chair, ENAR 2021 Spring Meeting, Online. Mar.2021
Session Chair, ENAR 2019 Spring Meeting, Philadelphia, PA. Mar.2019
Organizer for Biostatistics Student Seminar, Penn State Univ. Sep.2018-May.2020
Chair, Hershey Chinese Students and Scholars Association. Jan.2018-Sep.2019
Paper Review Service as a Reviewer:
Bioinformatics, Biometrics, Statistics in Medicine, Journal of Biopharmaceutical Statistics, Biometrical Journal, BMC Medical Research Methodology, European journal of neuroscience, Computers in Biology and Medicine, among others
- Invited talk. An Efficient Data Integration Scheme to Synthesize Information from Multiple Secondary Outcomes into the Main Data Analysis, SLAM, JHU, April 2022.
- Invited talk. Robust Schemes for Incorporating Multiple Auxiliary Information in Biomedical Studies, BRIGHT, UMD, Nov. 2021.
- Contributed paper presentation. A Generalized Weighting Scheme to Integrate Information from Multiple Auxiliary Records to the Main Study, ENAR Spring Meeting,
Baltimore, MA. Mar.2021
- Contributed paper presentation. Informative dynamic ODE-based-network learning (IDOL) from steady data, ENAR Spring Meeting, Nashville, TN. Mar.2020
- Paper award presentation. A robust consistent information criterion for model selection based on empirical likelihood, JSM, Denver, CO. Jul.2019
- Contributed paper presentation. A robust consistent information criterion for model selection based on empirical likelihood, ENAR Spring Meeting, Philadelphia, PA. Mar.2019
- Scholar award poster. A robust consistent information criterion for model selection based on empirical likelihood, The 74th Annual Deming Conference, Atlantic City, NJ. Dec.2018
- Paper award presentation. Empirical likelihood based criteria for model selection on marginal analysis of longitudinal data with dropout missingness, ICSA Applied Statistics Symposium, New Brunswick, NJ. Jun.2018
- Contributed paper presentation. Empirical likelihood based criteria for model selection on marginal analysis of longitudinal data with dropout missingness, ENAR Spring Meeting, Atlanta, GA. Mar.2018