Some new advances in precision medicine modeling

Earlier work has shown that similarity-based predictive models can improve upon predictive performance, as compared to using the entire training data to help build models, particular regarding model discrimination for binary responses. My collaborators and I have some updated results to share, regarding similarity-based modeling for joint consideration of model calibration and discrimination, as well as for dynamic prediction models. In addition, we have been developing transfer learning methods for targeted prediction. Properties of our methods will be investigated in comprehensive simulation studies, and we will demonstrate the methods through separate analyses of a publicly-available intensive care unit (ICU) database.

Collaborators: Keeley lsinghood, Minzee Kim, Tatiana Krikella, Subha Maity, Mengqi Xu Department of Statistics and Actuarial Science, and School of Public Health Sciences, University of Waterloo; Department of Statistical Sciences, University of Toronto

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.