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