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Activity Number: 579 - 30 Years Journey of Bayesian Adaptive Designs: From Bayesian Monitoring to Dynamic Treatment Regime — in Honor of Dr. Peter Thall’s 70th Birthday
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Biopharmaceutical Section
Abstract #312714
Title: Rethinking Prior Effective Sample Size
Author(s): Satoshi Morita*
Companies: Kyoto University
Keywords: Bayesian statistics; Bayesian clinical trial design; Bayesian data analysis; Effective sample size

A common concern in Bayesian data analysis is that an inappropriately informative prior may unduly influence posterior inferences. In the context of Bayesian clinical trial design, appropriately chosen priors are important to ensure that posterior-based decision rules have good frequentist properties. However, it is difficult to quantify prior information in all but the most stylized models. This issue may be addressed by quantifying the prior information in terms of a number of hypothetical patients, i.e., a prior effective sample size (ESS). ESS of a Bayesian parametric model was defined by Morita, Thall, and Mueller (2008, Biometrics). The ESS provides an easily interpretable index of the informativeness of a prior with respect to a given likelihood. However, the approach requires somewhat complicated analytical computations of the distance between the prior and posterior. This talk will review several alternative definitions of ESS proposed so far and will discuss several remained practical problems.

Authors who are presenting talks have a * after their name.

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