Bayesian Statistical Methods for Clinical Trial Design
Anna Heath, MMath, PhD
Assistant Professor | University of Toronto
Where: In-person | 2001 McGill College, Room 1203 |
Abstract
Appropriate trial design is key to determine accurate, unbiased 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 range of statistical solutions that can address these concerns, leading to feasible, nimble and highly relevant trials. This presentation will discuss some of these solutions and demonstrate the statistical methods used to implement the methods in practice.
Speaker Bio
Anna Heath is a Scientist in the Hospital for Sick Children (SickKids), Toronto, an Assistant Professor at the University of Toronto and an Honorary Research Fellow at University College London. Her research focuses on developing innovative statistical methods to address some of the key issues in the design of clinical 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 synthesis. Originally from the UK, she holds a PhD in Statistical Science from University College London and, since completing her PhD, has worked at SickKids on innovative paediatric clinical trials.
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