BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20251122T023133EST-1009u9AtGc@132.216.98.100 DTSTAMP:20251122T073133Z DESCRIPTION:Title: Bayesian Adaptive Basket Trial Design Using Model Averag ing.\n\nAbstract: Matt Psioda is Head of Statistical Innovation for Oncolo gy and Vaccines within GSK’s Statistics and Data Science Innovation Hub. I n that role\, he leads a small team of statistical consultants to support use of innovative study designs and advanced statistical methods in GSK st udies. His group works on a variety of applied and methodological research problems. Examples include extrapolating information on treatment effecti veness from adult to adolescent/pediatric settings\, design and analysis o f clinical trials with hybrid or external control arms\, and design and an alysis of adaptive basket and/or platform trials. Prior to joining GSK\, m ost recently Matt was on the faculty in the Department of Biostatistics at the University of North Carolina at Chapel Hill and was a Statistical Adv isor to the United States Food and Drug Administration’s Center for Drug E valuation and Research.\n\n\nWe discuss a Bayesian adaptive design methodo logy for oncology basket trials with binary endpoints using a Bayesian mod el averaging framework. Most existing methods seek to borrow information b ased on the degree of homogeneity of estimated response rates across all b askets. In reality\, an investigational product may only demonstrate activ ity for a subset of baskets\, and the degree of activity may vary across t he subset. A key benefit of our Bayesian model averaging approach is that it explicitly accounts for the possibility that any subset of baskets may have similar activity and that some may not. Our proposed approach perform s inference on the basket-specific response rates by averaging over the co mplete model space for the response rates\, which can include thousands of models. We present results that demonstrate that this computationally fea sible Bayesian approach performs favorably compared to existing state-of-t he-art approaches\, even when held to stringent requirements regarding fal se positive rates.\n\n \n\nZoom Link: /epi-biostat-oc ch/seminars-events/seminars/biostati...\n DTSTART:20221102T193000Z DTEND:20221102T203000Z SUMMARY:Matthew Psioda\, PhD\, GSK URL:/mathstat/channels/event/matthew-psioda-phd-gsk-34 3172 END:VEVENT END:VCALENDAR