Please join the Department of Mathematical Sciences for the Statistics Seminar scheduled from 11:30 a.m. to 12:30 p.m. on Friday, March 28, in person.

 Speaker: Professor Gang Li, University of California, Los Angeles

Title: Prediction Performance Measures for Time-to-Event Data

Abstract: Evaluating and validating the performance of prediction models is a crucial task in statistics, machine learning, and their diverse applications, including precision medicine. However, developing robust prediction performance measures, particularly for time-to-event data, poses unique challenges. In this talk, I will highlight how conventional performance metrics for time-to-event data, such as the C Index, Brier Score, and time-dependent AUC, may yield unexpected results when comparing prediction models/algorithms. I will then introduce a novel pseudo R-squared measure and demonstrate its utility as a prediction performance measure for both uncensored and right-censored time-to-event data. Additionally, I will discuss its extension to time-dependent prediction performance measures and to competing risks scenarios. Its effectiveness will be showcased through simulations and real-world examples.

Location: CHB C230

Zoom information:  Meeting ID: 961 4426 7415       Passcode: 85425454