A spatial Beta-Binomial model for clustered count data on dental caries

摘要

One of the most important indicators of dental caries prevalence is the total count of decayed, missing or filled (DMF) 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 neighboring teeth). In this paper, we develop a multivariate spatial Beta-Binomial (BB) model for these data that accommodates both over-dispersion as well as latent spatial associations. Using a Bayesian paradigm, the re-parameterized marginal mean (as well as variance) under the BB framework are modeled using a regression on subject/tooth-specific co-variables and a conditionally autoregressive (CAR) 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 modeling spatial associations.