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DTSTAMP:20251105T094720Z
DESCRIPTION:Anna Heath\, MMath\, PhD\n\nAssistant Professor | University of
  Toronto\n\nWhere: In-person | 2001 9IÖÆ×÷³§Ãâ·Ñ College\, Room 1203 | Zoom\n\nA
 bstract\n\nAppropriate trial design is key to determine accurate\, unbiase
 d results from clinical trials. However\, traditional methods for clinical
  trial design can lead to infeasible sample sizes\, be slow and inflexible
 \, leading to delays in getting effective treatments to patients and focus
  on outcomes that do not support decision making. However\, there are a ra
 nge of statistical solutions that can address these concerns\, leading to 
 feasible\, nimble and highly relevant trials. This presentation will discu
 ss some of these solutions and demonstrate the statistical methods used to
  implement the methods in practice.\n\nSpeaker Bio\n\nAnna Heath is a Scie
 ntist in the Hospital for Sick Children (SickKids)\, Toronto\, an Assistan
 t Professor at the University of Toronto and an Honorary Research Fellow a
 t University College London. Her research focuses on developing innovative
  statistical methods to address some of the key issues in the design of cl
 inical trial\, particularly in paediatrics. Her methods are based on many 
 concepts including Bayesian decision analysis\, clinical trial analysis\, 
 Bayesian computational methods\, health economic modelling and evidence sy
 nthesis. Originally from the UK\, she holds a PhD in Statistical Science f
 rom University College London and\, since completing her PhD\, has worked 
 at SickKids on innovative paediatric clinical trials.\n\nWebsite: https://
 lab.research.sickkids.ca/heath/\; https://www.sickkids.ca/en/staff/h/anna-
 heath/\n
DTSTART:20230222T203000Z
DTEND:20230222T213000Z
SUMMARY:Bayesian Statistical Methods for Clinical Trial Design
URL:/epi-biostat-occh/channels/event/bayesian-statisti
 cal-methods-clinical-trial-design-345571
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