A spatial beta-binomial model for clustered count data on dental caries

Dipankar Bandyopadhyay, Brian J. Reich, Elizabeth H. Slate

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)85-102
Number of pages18
JournalStatistical methods in medical research
Volume20
Issue number2
DOIs
StatePublished - Apr 2011

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