Title: Functional Individualized Treatment Regimes with Imaging Features

Speaker:Xinyi Li (Clemson University)

Time:9:00-10:00 2023/09/14(Thursday)

Platform: Tencent Meeting ID 794-950-692

Abstract:Precision medicine seeks to discover an optimal personalized treatment plan and thereby provide informed and principled decision support, based on the characteristics of individual patients. With recent advancements in medical imaging, it is crucial to incorporate patient-specific imaging features in the study of individualized treatment regimes. In this talk, the speaker will discuss a novel, data-driven method to construct interpretable image features which can be incorporated, along with other features, to guide optimal treatment regimes. The proposed method treats imaging information as a realization of a stochastic process, and employs smoothing techniques in estimation. It will be shown that the proposed estimators are consistent under mild conditions. The proposed method is applied to a dataset provided by the Alzheimer's Disease Neuroimaging Initiative.

About the speaker

Dr. Xinyi Li is currently an assistant professor in the School of Mathematical and Statistical Sciences at Clemson University. Before joining Clemson in 2020, Dr. Li was a postdoctoral fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI), joint with the University of North Carolina at Chapel Hill. She obtained her Ph.D. in statistics at Iowa State University in 2018. Her research interests include precision medicine, functional data analysis, non-/semi-parametric high-dimensional regression, spatio-temporal analysis, with application to statistical genetics, neuroimaging, and public health. Dr. Li is the principal investigator of an NSF award, and a recipient of the IMS new researcher travel award (2019). She serves on the Early Career Advisory Board for the Journal of Multivariate Analysis.