时 间：2024年7月11日15:00 - 16:00
地 点：普陀校区理科大楼A1514
报告人：Yu Shen 得克萨斯大学安德森癌症中心教授
主持人：谌自奇 华东师范大学教授
摘 要：
In comparative effectiveness research and precision medicine for rare types of cancer, it is desirable to combine multiple sources of data, e.g., the primary cohort data containing detailed tumor profiles together with aggregate information derived from cancer registry databases. Such integration of data may improve statistical efficiency in estimation and precision in prediction, but also pose statistical challenges for incomparability between different sources of data. We develop the adaptive estimation procedures, which used the combined information to determine the degree of information borrowing from the aggregate data of the external resource or individual-level data source. We apply the proposed method to evaluate the long-term effect of several commonly used treatments for inflammatory breast cancer by tumor subtypes, while combining the inflammatory breast cancer patient cohort at MD Anderson Cancer Center and external cancer registry data.
报告人简介：
Yu Shen, Ph.D., Professor and Interim Chair, Department of Biostatistics Dr. Yu Shen is a Professor of Biostatistics at the University of Texas M.D. Anderson Cancer Center, where she holds the Conversation with a Living Legend Professorship. She obtained her PhD in biostatistics from University of Washington and has been a faculty member of MDA since 1995. She is an alumna of East China Normal University. Her primary interest is to develop state-of-the-art statistical methods and models to address research questions in cancer early detection, and personalized cancer treatment. She has developed statistical methods in the areas of data integration, modeling the natural history of cancer, adaptive clinical trial designs, and modeling survival data subject to biased sampling, with continuous NIH grant support for her statistical method development and health economic research. She is an elected Fellow of the American Statistical Association and the Institute of Mathematics Statistics.