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DTSTAMP:20260914T021407Z
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: https://www.mcgill.ca/epi-biostat-oc
 ch/seminars-events/seminars/biostati...\n
DTSTART:20221102T193000Z
DTEND:20221102T203000Z
SUMMARY:Matthew Psioda\, PhD\, GSK
URL:https://www.mcgill.ca/mathstat/channels/event/matthew-psioda-phd-gsk-34
 3172
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