Speaker
Dr. Lukas Pin
Medical Research Council (MRC) Biostatistics Unit
University of Cambridge
Date
Friday, November 8, 2024
11:00 A.M. – 12:00 P.M. ET
Location
Nguyen Engineering Building
Room 1109
4511 Patriot Circle
Fairfax, Virginia 22030
A Broad Overview of Response-Adaptive-Randomization With a Novel Application and Some New Insights
Abstract
Response-Adaptive Randomisation (RAR) enhances clinical trials by adjusting patient allocation based on accumulating data, achieving patient-oriented goals and/or statistical efficiency. This talk offers a concise overview of RAR, exploring influential designs such as the (Randomised) Play-the-Winner Rule, Bayesian approaches, and optimal allocation strategies. We will address common questions to clarify misconceptions. Moreover, we discuss the application of Bayesian RAR in early-phase dose-finding trials, as well as optimal RAR in confirmatory trials. Introducing a novel application, we provide new insights that extend current methodologies. Attendees will gain a clear understanding of RAR's core concepts, practical implementations, and innovative advancements shaping future research around RAR.
About the Speaker
Lukas Pin is a third-year Biostatistics PhD student at the MCR Biostatistics Unit, University of Cambridge, where he is supervised by Dr. Sofía S. Villar. His research focuses on response adaptive designs, nonparametric statistics, randomization-based inference and their application in the context of clinical trials.Lukas holds a Master's degree in Statistics from Humboldt University of Berlin, where he worked under the guidance of Prof. Brunner and Prof. Konietschke, and his thesis involved deriving a novel method for constructing confidence intervals for the Mann-Whitney effect. Prior to his master's degree, Lukas completed two Bachelor degrees in Mathematics and Economics at the University of Bonn. Currently, Lukas's research is centered around developing a novel nonparametric response adaptive randomization method suitable for multi-armed trials with small sample sizes and potentially continuous endpoints. Working on one of those methods with Prof. Rosenberger is the reason for his visit to George Mason.
Event Organizer
Jonathan L. Auerbach
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University
Ben Seiyon Lee
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University