Michael R. Kosorok, PhD, is W. R. Kenan, Jr. Distinguished Professor of biostatistics, and professor of statistics and operations research at UNC-Chapel Hill.
His biostatistical expertise includes machine learning, precision medicine, clinical trials, dynamic treatment regimes, data science, big data, data mining, bioinformatics, empirical processes, semiparametric inference, Monte Carlo methods, and he has written a major text on the theoretical foundations of these and related areas in biostatistics (Kosorok, 2008) as well as co-edited (with Erica E. M. Moodie) a research monograph on dynamic treatment regimes and precision medicine.
He also has expertise in the application of biostatistics to biomedical research, including cancer and cystic fibrosis. In particular, he is the contact principal investigator on an NCI program project grant (P01 CA142538), which focuses on statistical methods for novel cancer clinical trials in precision medicine, including biomarker discovery and dynamic treatment regimes. He has pioneered machine learning and data mining tools for these and related areas.
Title: Tree based precision medicine for right censored data
Abstract: Estimating individualized treatment rules is a central task of personalized or precision medicine. In this presentation, we develop a new, tree based nonparametric method for discovering high dimensional treatment rules when the primary outcome of interest is right censored. The new approach avoids both inverse probability of censoring weighting and semiparametric modeling of either the censoring or failure times. We establish consistency and convergence rates for our proposed estimators. In simulation studies, our estimators demonstrate improved performance compared to existing methods. We also illustrate the proposed method on a phase III clinical trial of non-small cell lung cancer.