Memorial Session for Myles Hollander

In this talk I will provide some personal recollections of the late-Professor Myles Hollander: my teacher, my mentor and adviser, as a second father to me, my collaborator, a professional colleague, and my friend. I will recall some anecdotes about his interactions with his students and colleagues, his style of mentoring and advising students, his supportive nature to his students' professional careers, and his being a great friend to many of us. I will also describe some of the research projects that we worked on over the years, and how it had impacted my own professional trajectory. 

Evaluating and validating the performance of prediction models is a fundamental task in statistics, machine learning, and their diverse applications. However, developing robust performance metrics for competing risks time-to-event data poses unique challenges. We first highlight how certain conventional predictive performance metrics for competing risks time-to-event data, such as the C-index, Brier Score, and time-dependent AUC, can yield unexpected results when comparing predictive performance between different prediction models. To address this research gap, we introduce a novel time-dependent pseudo R-squared measure to evaluate the predictive performance of a predictive cumulative incidence function over a restricted time domain under right-censored competing risks time-to-event data. Specifically, we first propose a population-level time-dependent pseudo R-squared measures for the competing risk event of interest and then define their corresponding sample versions based on right-censored competing risks time-to-event data. We investigate the asymptotic properties of the proposed measure and demonstrate its advantages over conventional metrics through comprehensive simulation studies and three real-data applications.


Authors: Zian Zhaung, Wen Su, Eric Kawaguchi, and Gang Li*

*Presenter: Gang Li, Ph.D.
Professor of Biostatistics and Computational Medicine, University of California at Los Angeles