BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20260404T084408EDT-0433HuUS02@132.216.98.100
DTSTAMP:20260404T124408Z
DESCRIPTION:Special Seminar: Monday\, November 24\, 2025\, from 3:30 to 4:3
 0 pm in Room 1203\n	\n	Ying Yuan\, PhD\n\nBettyann Asche Murray Distinguishe
 d Professor\n	Chair of the Department of Biostatistics |\n	MD Anderson Cance
 r Center\, University of Texas\n\nWHEN: Monday\, November 24\, 2025\, from
  3:30 to 4:30 p.m.\n	WHERE: Hybrid | 2001 McGill College Avenue\, Rm 1203\;
  Zoom\n	NOTE: Ying Yuan will be presenting in-person at SPGH \n	 \n\nAbstrac
 t\n\nMixture priors provide an intuitive way to incorporate historical dat
 a while accounting for potential prior-data conflict by combining an infor
 mative prior with a non-informative prior. However\, pre-specifying the mi
 xing weight for each component remains a crucial challenge. Ideally\, the 
 mixing weight should reflect the degree of prior-data conflict\, which is 
 often unknown beforehand\, posing a significant obstacle to the applicatio
 n and acceptance of mixture priors. To address this challenge\, we introdu
 ce self-adapting mixture (SAM) priors that determine the mixing weight usi
 ng likelihood ratio test statistics or Bayes factors. SAM priors are data-
 driven and self-adapting\, favoring the informative (non-informative) prio
 r component when there is little (substantial) evidence of prior-data conf
 lict. Consequently\, SAM priors achieve dynamic information borrowing. We 
 demonstrate that SAM priors exhibit desirable properties in both finite an
 d large samples and achieve information-borrowing consistency. We develope
 d R package 'SAMprior' to facilitate the use of SAM priors.\n\n\n	Speaker B
 io\n\nYing Yuan is the Bettyann Asche Murray Distinguished Professor and C
 hair of the Department of Biostatistics at the University of Texas MD Ande
 rson Cancer Center. Dr. Yuan is internationally renowned for his pioneerin
 g research in innovative Bayesian adaptive designs\, including early-phase
  trials\, seamless trials\, biomarker-guided trials\, and basket and platf
 orm trials. The designs and software developed by Dr. Yuan’s lab (www.tria
 ldesign.org) have been widely adopted by medical research institutes and p
 harmaceutical companies. Among these\, the BOIN design\, developed by Dr. 
 Yuan’s team\, is a groundbreaking oncology dose-finding method recognized 
 by the FDA as a fit-for-purpose drug development tool. Dr. Yuan is also an
  elected Fellow of the American Statistical Association and the lead autho
 r of two seminal books: Bayesian Designs for Phase I-II Clinical Trials an
 d Model-Assisted Bayesian Designs for Dose Finding and Optimization\, both
  published by Chapman & Hall/CRC.\n
DTSTART:20251124T203000Z
DTEND:20251124T213000Z
SUMMARY:SAM: Self-adapting Mixture Prior to Dynamically Borrow Information 
 from Historical Data in Clinical Trials
URL:https://www.mcgill.ca/channels/channels/event/sam-self-adapting-mixture
 -prior-dynamically-borrow-information-historical-data-clinical-trials-3689
 18
END:VEVENT
END:VCALENDAR