Xu Shi, PhD
- John G. Searle Assistant Professor, Biostatistics
Dr. Shi is interested in developing novel statistical methods that provide insights from high volume and high variability administrative healthcare data such as electronic health records (EHR) and claims data. She develops scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems. She also develops causal inference methods that harness the full potential of EHR data to address comparative effectiveness and safety questions. She co-leads the Causal Inference Core of the FDA's Sentinel Initiative Innovation Center to develop statistical methods to monitor the safety of FDA-regulated medical products and explore novel ways to utilize information from distributed EHR data partners.
- PhD, Biostatistics, University of Washington, 2017
- B.S., Mathematics, Zhejiang University, China, 2012
Electronic health records, causal inference, data corruption, record linkage, machine translation, semiparametric efficiency theory, postmarketing safety surveillance, healthcare policy
Accounting for Hidden Bias in Vaccine Studies: A Negative Control Framework
Using Unsupervised Learning to Generate Code Mapping Algorithms to Harmonize Data Across Data Systems
Automated Harmonization of Multi-Institutional Electronic Health Records Data
Phenotyping Patients with Brain Injury Associated Fatigue and Altered Cognition (BIAFAC) Across Two Institutions
Shi X, Li Q, Mukherjee B. (2022). Current Challenges with the Use of Test-Negative Designs for Modeling COVID-19 Vaccination and Outcomes. American Journal of Epidemiology, in press.
Shi X, Pan Z, and Miao W. (2021). Data Integration in Causal Inference. WIRES Computational Statistics, in press.
Shi X, Li X, and Cai T. (2021) Spherical regression under mismatch corruption with application to automated knowledge translation. Journal of the American Statistical Association: Theory and Methods, 116(536), 1953-1964.
Shi X, Miao W, and Tchetgen Tchetgen EJ (2020). A selective review of negative control methods in epidemiology. Current Epidemiology Reports, in press.
Shi X, Miao W, and Tchetgen Tchetgen EJ. (2020). Multiply robust causal inference with double negative control adjustment for categorical unmeasured confounding. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(2):521-540.
Shi X, Pashova H, and Heagerty PJ. (2017). Comparing healthcare utilization patterns via global differences in the endorsement of current procedural terminology codes. Annals of Applied Statistics, 11(3):1349-1374.
Chen C, Haupert SR, Zimmermann L, Shi X, Fritsche LG, Mukherjee B (2022). Global Prevalence of Post COVID-19 Condition or Long COVID: A Meta-Analysis and Systematic Review. Journal of Infectious Diseases, in press.
Beam AL, Kompa B, Fried I, Palmer N, Shi X, Cai T, and Kohane I. (2020). Medical Concept Embeddings Estimated from Massive Sources of Biomedical Data. Pacific Symposium on Biocomputing (PSB), 25: 295-306.
Areas of Expertise: Biostatistics