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

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Abstract

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.
Lingua originaleEnglish
pagine (da-a)473-480
Numero di pagine8
RivistaCommunity Dentistry and Oral Epidemiology
Volume41
Stato di pubblicazionePublished - 2013

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Dental Caries
Statistical Models
Epidemiology
Bayes Theorem
Deciduous Tooth
Hygiene
Uncertainty
Datasets
Education

All Science Journal Classification (ASJC) codes

  • Dentistry(all)
  • Public Health, Environmental and Occupational Health

Cita questo

@article{78fba2fbb3a44b2686882f6ef2d50424,
title = "Can bayesian models play a role in dental caries epidemiology? Evidence from an application to the BELCAP data set",
abstract = "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.",
keywords = "Bayesian analysis; Belo Horizonte Caries Prevention; bounded data; dmft; informative prior; zero-inflated betabinomial; zero-inflated binomial",
author = "Alberto Firenze and Domenica Matranga and Angela Vullo",
year = "2013",
language = "English",
volume = "41",
pages = "473--480",
journal = "Community Dentistry and Oral Epidemiology",
issn = "0301-5661",
publisher = "Blackwell Munksgaard",

}

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 -