Xuan Wang

Xuan Wang, PhD

Academic Information

Departments Primary - Population Health Sciences

Research Interests

  • Regression Model and Measurement Error Model
  • Complex Survival Data Analysis
  • Surrogate Evaluation
  • Casual Inference and Missing Data Analysis
  • Electronic Health Record Data Analysis

Research Statement

My research interests include statistical methods for surrogate validation, causal inference and missing data analysis, complex survival data analysis, supervised learning, semi-supervised learning, federated transfer learning, etc. Meanwhile, I make great effect in applying these noval statistical methods to analyze real world data, especially electronic health records (EHR) data. For a full list of my publications, please see

https://scholar.google.com/citations?hl=en&user=sH8TVSoAAAAJ&view_op=list_works&sortby=pubdate

https://www.researchgate.net/profile/Xuan-Wang-96

Education History

Postdoctoral Fellowship Harvard University
 Postdoctoral Fellow
University of Washington
 Postdoctoral Fellow
Doctoral Training Academy of Mathematics and Systems Science of the Chinese Academy of Sciences
 PhD
Beijing Jiaotong University
 BS

Selected Publications

Journal Article

  1. Wang L, Wang X, Liao KP, Cai T (2024). Semi-supervised Transfer Learning for Evaluation of Model Classification Performance. Biometrics, 80(1).
  2. Wang X, Panickan VA, Cai T, Xiong X, Cho K, Cai T, Bourgeois FT (2023). Endovascular aneurysm repair devices as a use case for postmarketing surveillance of medical devices. JAMA internal medicine, 183(10), 1090-1097.
  3. Wang X, Parast L, Han L, Tian L, Cai T (2022). Robust approach to combining multiple markers to improve surrogacy. Biometrics, 79(2), 788-798. (Read full article)
  4. Hou J, Chan SF, Wang X, Cai T (2021). Risk prediction with imperfect survival outcome information from electronic health records. Biometrics, 79(1), 190-202. (Read full article)
  5. Wang X, Claggett BL, Tian L, Malachias MVB, Pfeffer MA, Wei LJ (2023). Quantifying and Interpreting the Prediction Accuracy of Models for the Time of a Cardiovascular Event-Moving Beyond C Statistic: A Review. JAMA Cardiol, 8(3), 290-295. (Read full article)
  6. Wang X, et al (2022). SurvMaximin: Robust federated approach to transporting survival risk prediction models. J Biomed Inform, 134, 104176. (Read full article)
  7. Wang X, Kim DH, Wei LJ (2021). Quantifying and Interpreting Efficacy of Reduced-Intensity Chemotherapy With Oxaliplatin and Capecitabine on Cancer Control for Advanced Gastroesophageal Cancer Among an Older Population. JAMA oncology, 7(11).
  8. Wang X, Zheng Y, Jensen MK, He Z, Cai T (2021). Biomarker evaluation under imperfect nested case-control design. Stat Med, 40(18), 4035-4052. (Read full article)
  9. Chan S, Wang X, Jazi I, Peskoe S, Zheng Y, Cai T (2020). Developing and evaluating risk prediction models with panel current status data. Biometrics, 77(2), 599-609. (Read full article)
  10. Wang X, Parast L, Tian LU, Cai T (2019). Model-free approach to quantifying the proportion of treatment effect explained by a surrogate marker. Biometrika, 107(1), 107-122. (Read full article)