A spatial beta-binomial model for clustered count data on dental caries.
One of the most important indicators of dental caries prevalence is the total count of decayed, missing or filled surfaces in a tooth. These count data are often clustered in nature (several count responses clustered within a subject), over-dispersed as well as spatially referenced (a diseased tooth might be positively influencing the decay process of a set of neighbouring teeth). In this article, we develop a multivariate spatial betabinomial (BB) model for these data that accommodates both over-dispersion as well as latent spatial associations. Using a Bayesian paradigm, the re-parameterised marginal mean (as well as variance) under the BB framework are modelled using a regression on subject/tooth-specific co-variables and a conditionally autoregressive prior that models the latent spatial process. The necessity of exploiting spatial associations to model count data arising in dental caries research is demonstrated using a small simulation study. Real data confirms that our spatial BB model provides a superior estimation and model fit as compared to other sub-models that do not consider modelling spatial associations.
Division of Biostatistics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. bandyopd@musc.edu
Dipankar
D
Bandyopadhyay
Brian J
BJ
Reich
Elizabeth H
EH
Slate
D001499:Bayes Theorem; D056808:Biostatistics; D003627:Data Interpretation, Statistical; D003731:Dental Caries / Q000453:epidemiology* / Q000473:pathology*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015999:Multivariate Analysis