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Activity Number: 352
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304944
Title: A Comparison of Power in Normal Theory Approximation to Bayesian MCMC Procedures
Author(s): Jiang Yuan*+ and John W. Seaman, PhD and James Stamey
Companies: Baylor University and Baylor University and Baylor University
Address: One Bear Place, #97140, Waco, TX, 76798-7140, United States
Keywords: normal theory approximation ; logistic regression ; binary diagnostic test ; MCMC simulation

Bayesian sample size determination can be computationally intensive for models where Markov chain Monte Carlo (MCMC) methods are commonly used for inference. We present a normal theory approximation as an alternative to the time consuming MCMC simulations for sample size determination for a binary regression with misclassification. Cheng, Stamey and Branscum (2009) develop a Bayesian approach to average power calculations in binary regression models applied to the common medical scenario where a patient's disease status is not known. We use our normal theory approximation to speed up such sample size determination and compare power and computational time for both. We do this for methods using one and two diagnostic tests. While we focus on the case of a misclassified response, our method is applicable to other complicated scenarios as well, such as uncontrolled confounding and covariate measurement error.

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