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Activity Number: 484
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #318879
Title: Nonparametric Spatial Models for Clustered Ordered Periodontal Data
Author(s): Dipankar Bandyopadhyay* and Antonio Canale
Companies: Virginia Commonwealth University and University of Turin/Collegio Carlo Alberto
Keywords: G-Wishart ; Multivariate ordinal ; Nonparametric Bayes ; Probit stick-breaking ; Periodontal Disease ; Spatial association

Clinical attachment level is regarded as the most popular measure to assess periodontal disease(PD). These probed tooth-site level measures are usually rounded and recorded as whole numbers producing clustered, error-prone ordinal responses representing some ordering of the underlying PD state. In addition, PD progression maybe spatially-referenced. In this talk, we present a Bayesian multivariate probit framework for these ordinal responses, where the cut-point parameters linking the observed ordinal levels to the latent disease process can be fixed in advance. The latent spatial association characterizing conditional independence under Gaussian graphs is introduced via a nonparametric Bayesian approach motivated by the probit stick-breaking process, where the components of the stick-breaking weights follows a multivariate Gaussian density with the precision matrix distributed as G-Wishart. Both simulation studies and application to a real data reveal the advantages of this computationally simple, yet robust and flexible framework to capture the latent disease status, leading to a natural clustering of tooth-sites and subjects with similar PD status, beyond spatial clustering.

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

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