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

• Published in: Statistical methods in medical research Vol. 20; no. 2; pp. 85 - 102
• Main Authors: Bandyopadhyay, Dipankar, Reich, Brian J, Slate, Elizabeth H
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.04.2011; Sage Publications Ltd
• Subjects: Associations; Bayes Theorem; Bayesian analysis; Biostatistics; Caries; Data Interpretation, Statistical; Dental caries; Dental Caries - epidemiology; Dental Caries - pathology; Humans; Likelihood Functions; Marginal; Models, Statistical; Multivariate Analysis; Necessity; Prevalence; Simulation; Teeth
• ISSN: 0962-2802; 1477-0334; 1477-0334
• DOI: 10.1177/0962280210372453

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.