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Activity Number: 231
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #320800
Title: A Note on Posterior Predictive Assessment to Assess Model Fit
Author(s): Arkendu S. Chatterjee* and Dandan Xu and Michael Daniels
Companies: Novartis and University of Florida and The University of Texas at Austin
Keywords: Missing data ; Model diagnostics ; Posterior predictive distribution

We examine two posterior predictive distribution based approaches to assess model fit for incomplete longitudinal data. The first approach assesses fit based on replicated complete data as advocated in Gelman et al.(2005.The second approach assesses fit based on replicated observed data.Differences between the two approaches are discussed and an analytic example is presented for illustration and understanding. Both checks are applied to data from a longitudinal clinical trial.The proposed checks can easily be implemented in standard software like (Win)BUGS/JAGS/Stan.

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

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