Rate My Professor Xuan Wang

Wang's research specializations include statistical methods for surrogate validation, causal inference and missing data analysis, complex survival data analysis, supervised learning, semi-supervised learning, and federated transfer learning. She focuses on applying these novel statistical approaches to real-world data, particularly electronic health records (EHR) to enhance risk prediction and clinical decision-making. Key publications demonstrate her contributions to the field. In 2023, she co-authored 'Quantifying and Interpreting the Prediction Accuracy of Models for the Time of a Cardiovascular Event-Moving Beyond C Statistic: A Review' in JAMA Cardiology. That same year, 'Risk prediction with imperfect survival outcome information from electronic health records' appeared in Biometrics. In 2022, 'SurvMaximin: Robust federated approach to transporting survival risk prediction models' was published in the Journal of Biomedical Informatics. Additional notable works include 'Endovascular Aneurysm Repair Devices as a Use Case for Postmarketing Surveillance of Medical Devices' in JAMA Internal Medicine (2023) and 'Semiparametric Joint Modeling to Estimate the Treatment Effect on a Longitudinal Surrogate with Missing Data' forthcoming in Biometrics (2025). With 48 selected publications, her work underscores advancements in biostatistics for population health sciences.