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SDP Webinar - January 2024

A Decision Analytic Framework for Bayesian Updating of Probability of Success in Clinical Trials

Our Q1 session features Tom Keelin who will demonstrate how to use data from a pilot investigation to update Metalogs in closed form and to generate posterior Bayesian estimates of clinical trial POS. This new approach performs straight-forward Bayesian inference without the need for intensive numerical methods such as markov-chain monte-carlo (MCMC) modelling. Tom will walk through a quantitative example to clearly demonstrate the approach to guide investment decision-making for a phase 3 clinical trial. In addition, he will discuss how this same approach can be easily generalized for Bayesian inference in other industries from estimating trout sizes to golf shot dispersion patterns. Please join us for this exciting presentation.
 

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