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September 2012 Prior Effective Sample Size in Conditionally Independent Hierarchical Models
Satoshi Morita, Peter F. Thall, Peter Müller
Bayesian Anal. 7(3): 591-614 (September 2012). DOI: 10.1214/12-BA720


Prior effective sample size (ESS) of a Bayesian parametric model was defined by Morita, et al. (2008, Biometrics, 64, 595-602). Starting with an ɛ-information prior defined to have the same means and correlations as the prior but to be vague in a suitable sense, the ESS is the required sample size to obtain a hypothetical posterior very close to the prior. In this paper, we present two alternative definitions for the prior ESS that are suitable for a conditionally independent hierarchical model. The two definitions focus on either the first level prior or second level prior. The proposed methods are applied to important examples to verify that each of the two types of prior ESS matches the intuitively obvious answer where it exists. We illustrate the method with applications to several motivating examples, including a single-arm clinical trial to evaluate treatment response probabilities across different disease subtypes, a dose-finding trial based on toxicity in this setting, and a multicenter randomized trial of treatments for affective disorders.


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Satoshi Morita. Peter F. Thall. Peter Müller. "Prior Effective Sample Size in Conditionally Independent Hierarchical Models." Bayesian Anal. 7 (3) 591 - 614, September 2012.


Published: September 2012
First available in Project Euclid: 28 August 2012

zbMATH: 1330.62147
MathSciNet: MR2981629
Digital Object Identifier: 10.1214/12-BA720

Rights: Copyright © 2012 International Society for Bayesian Analysis


Vol.7 • No. 3 • September 2012
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