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Activity Number: 187
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #317273
Title: Spatial Skew-Normal/Independent Models for Clustered Periodontal Data with Nonrandom Missingness
Author(s): Dipankar Bandyopadhyay* and Victor Lachos and Marcos Prates and Brian J. Reich
Companies: University of Minnesota and University of Campinas and Universidade Federal de Minas Gerais and North Carolina State University
Keywords: skew ; thick-tails ; skew-normal/independent ; spatial ; clustered data

Periodontal studies generate clustered data (viz. clinical attachment level, or CAL, per tooth-site, in mm) that are routinely analyzed using linear mixed models assuming normality of the random effects and errors. However, a careful look reveals that these data might exhibit skewness and tail behavior. In addition, periodontal progression might be spatially referenced, i.e., diseased tooth-sites are proximally located. Also, the presence/absence of a tooth is informative, given the number and location of missing teeth informs about the periodontal health in that region. In this talk, we develop a Bayesian (shared) random effects model for CAL responses and binary presence/absence status of a tooth. The random effects are modeled using a spatial skew-normal/independent (S-SNI) distribution, whose covariance follows a conditionally autoregressive (CAR) density. Both simulation studies and application to a real data examining periodontal health status of Gullah-speaking African-Americans reveal the advantages of our proposition over the available standard alternatives.

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

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