Abstract
| Lingua originale | English |
|---|---|
| pagine (da-a) | 473-480 |
| Numero di pagine | 8 |
| Rivista | Community Dentistry and Oral Epidemiology |
| Volume | 41 |
| Stato di pubblicazione | Published - 2013 |
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All Science Journal Classification (ASJC) codes
- Dentistry(all)
- Public Health, Environmental and Occupational Health
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Can bayesian models play a role in dental caries epidemiology? Evidence from an application to the BELCAP data set. / Firenze, Alberto; Matranga, Domenica; Vullo, Angela.
In: Community Dentistry and Oral Epidemiology, Vol. 41, 2013, pag. 473-480.Risultato della ricerca: Article
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TY - JOUR
T1 - Can bayesian models play a role in dental caries epidemiology? Evidence from an application to the BELCAP data set
AU - Firenze, Alberto
AU - Matranga, Domenica
AU - Vullo, Angela
PY - 2013
Y1 - 2013
N2 - ObjectivesThe 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. MethodsThe data set used in this study was the Belo Horizonte Caries Prevention (BELCAP) study (Bohning etal. (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. ResultsWith 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. DiscussionIn 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.
AB - ObjectivesThe 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. MethodsThe data set used in this study was the Belo Horizonte Caries Prevention (BELCAP) study (Bohning etal. (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. ResultsWith 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. DiscussionIn 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.
KW - Bayesian analysis; Belo Horizonte Caries Prevention; bounded data; dmft; informative prior; zero-inflated betabinomial; zero-inflated binomial
UR - http://hdl.handle.net/10447/95965
M3 - Article
VL - 41
SP - 473
EP - 480
JO - Community Dentistry and Oral Epidemiology
JF - Community Dentistry and Oral Epidemiology
SN - 0301-5661
ER -