Can bayesian models play a role in dental caries epidemiology? Evidence from an application to the BELCAP data set

6533b7d5fe1ef96bd1263ca2

RESEARCH PRODUCT

Can bayesian models play a role in dental caries epidemiology? Evidence from an application to the BELCAP data set

Domenica Matranga Alberto Firenze Angela Vullo

subject

Male bounded data Best fitting Bayesian probability Deviance (statistics)informative prior Dental Caries Settore MED/42 - Igiene Generale E Applicata Settore MED/01 - Statistica Medica Overdispersion Prior probability Statistics Humans Medicine Child General Dentistry Bayesian analysi dmft DMF Index business.industry Belo Horizonte Caries Prevention zero-inﬂated betabinomial Caries epidemiology Public Health Environmental and Occupational Health Bayes Theorem Statistical model Regression zero-inﬂated binomial Female business Algorithms Brazil Bayesian analysis; Belo Horizonte Caries Prevention; bounded data; dmft; informative prior; zero-inﬂated betabinomial; zero-inﬂated binomial

description

Objectives The aim of this study was to show the potential of Bayesian analysis in statistical modelling of dental caries data. Because of the bounded nature of the dmft (DMFT) index, zero-inflated binomial (ZIB) and beta-binomial (ZIBB) models were considered. The effects of incorporating prior information available about the parameters of models were also shown. Methods The data set used in this study was the Belo Horizonte Caries Prevention (BELCAP) study (Bohning et al. (1999)), consisting of five variables collected among 797 Brazilian school children designed to evaluate four programmes for reducing caries. Only the eight primary molar teeth were considered in the data set. A data augmentation algorithm was used for estimation. Firstly, noninformative priors were used to express our lack of knowledge about the regression parameters. Secondly, prior information about the probability of being a structural zero dmft and the probability of being caries affected in the subpopulation of susceptible children was incorporated. Results With noninformative priors, the best fitting model was the ZIBB. Education (OR = 0.76, 95% CrI: 0.59, 0.99), all interventions (OR = 0.46, 95% CrI: 0.35, 0.62), rinsing (OR = 0.61, 95% CrI: 0.47, 0.80) and hygiene (OR = 0.65, 95% CrI: 0.49, 0.86) were demonstrated to be factors protecting children from being caries affected. Being male increased the probability of being caries diseased (OR = 1.19, 95% CrI: 1.01, 1.42). However, after incorporating informative priors, ZIB models' estimates were not influenced, while ZIBB models reduced deviance and confirmed the association with all interventions and rinsing only. Discussion In our application, Bayesian estimates showed a similar accuracy and precision than likelihood-based estimates, although they offered many computational advantages and the possibility of expressing all forms of uncertainty in terms of probability. The overdispersion parameter could expound why the introduction of prior information had significant effects on the parameters of the ZIBB model, while ZIB estimates remained unchanged. Finally, the best performance of ZIBB compared to the ZIB model was shown to catch overdispersion in data.

year	journal	country	edition	language
2012-11-14	Community Dentistry and Oral Epidemiology

https://doi.org/10.1111/cdoe.12044