Bayesian model with application to a study of dental caries

Community Dentistry and Oral Epidemiology, 2013

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 (Bö hning 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.

Pesquisa Brasileira em Odontopediatria e Clínica Integrada, 2017

To use the Bayesian statistical Model approach to predict the most important socio-demographic and occlusal factors pertinent to high prevalence of ECC. Material and Methods: A questionnaire and an oral examination was conducted on children who attended a pediatric dental clinic in Nairobi during the period of study. The parents provided information on socio-demographic and oral habits of the children. The oral examination for presence of dental caries was recorded for each child. Descriptive statistics were obtained for dental caries, oral hygiene, using plaque score, and malocclusion. The results of the questionnaire and presence of dental caries were analyzed and the results subjected to Bayesian statistical analysis to determine any predictive factors for ECC. Results: 55% of the children had plaque accumulating on more than one third but less than two thirds of tooth surfaces. The highest plaque scores were reported among children whose fathers (48.2%) and mothers (42.0%) had completed secondary, and whose fathers were in non-formal employment 73.2%. The overall prevalence of dental caries in the study group was 95.5% with a mean dmft of 8.53 (+ 5.52 SD), with the male children having higher dmft 8.65 (SD+5.54) than the female children 8.37 (SD+ 5.50). The prevalence of malocclusion among children in the study was 55%. The majority had mesial step, 51.5% (n=140) and flush terminal plane 28.3% (n=77). Conclusion: The Bayesian Model, with a correct assumption, can be used to determine the important factors involved in high prevalence of ECC.

BioMed Research International

Background. Child mortality is a global health problem. The United Nations’ 2018 report on levels and trends on child mortality indicated that under-five mortality is one of the major public health problems in Ghana with a rate of 60 deaths per 1000 live births. To further mitigate this problem, it is important to identify the drivers of under-five mortality in order to achieve the United Nations SDG Goal 3 target 2. Methods. In this study, we investigated the effects of some selected risk factors on child mortality using data from the 2014 Ghana Demographic Health Survey. We modelled the relationship between child mortality and the risk factors using a logistic regression model under the frequentist and Bayesian frameworks. We used the Metropolis-Hastings Algorithm to simulate parameter estimates from the posterior distributions, and statistical analyses were carried out using STATA version 14.1. Results. Results from the frequentist framework are in line with those from the Bayesi...

Gastroenterology and Hepatology From Bed to Bench, 2012

Aim The aim of this study is to estimate oral cavity cancer mortality for Iranian population, using Bayesian approach in order to revise this misclassification. Background Mortality is a familiar projection to address the burden of cancers, but according to Iranian death registry, about 20% death statistics were still recorded in misclassified categories. Patients and methods We analyzed national death statistic reported by the Iranian Ministry of Health and Medical Education from 1995 to 2004 stratified by age group, sex, and cause of death are included in this analysis. Oral cavity cancer [ICD-10; C00-08] were expressed as the annual mortality rates/100,000, overall, by sex and by age group (<50 and ≥50 years of age) and age standardized rate (ASR). The Bayesian approach to correct and account for misclassification effects in Poisson count regression with a beta prior employed to estimate the mortality rate of EC in age and sex group. Results According to the Bayesian re-estima...

This research evaluates the risk of diabetes among 581 men and women with factors such as age, ethnicity, gender, physical activity, hypertension, body mass index, family history of diabetes, and waist circumference by applying the logistic regression model to estimate the coefficients of these variables. Significant variables determined by the logistic regression model were then estimated using the Bayesian logistic regression (BLR) model. A flat non-informative prior, together with a non- informative non- flat prior distribution were used. These results were compared with those from the frequentist logistic regression (FLR) based on the significant factors. It was shown that the Bayesian logistic model with the non-informative flat prior distribution and frequentist logistic regression model yielded similar results, while the non-informative non-flat model showed a different result compared to the (FLR) model. Hence, non-informative but not perfectly flat prior yielded better model than the maximum likelihood estimate (MLE) and Bayesian with the flat prior.

Journal of the Royal Statistical Society: Series C (Applied Statistics), 2005

We present an approach for correcting for interobserver measurement error in an ordinal logistic regression model taking into account also the variability of the estimated correction terms. The different scoring behaviour of the 16 examiners complicated the identification of a geographical trend in a recent study on caries experience in Flemish children (Belgium) who were 7 years old. Since the measurement error is on the response the factor 'examiner' could be included in the regression model to correct for its confounding effect. However, controlling for examiner largely removed the geographical east-west trend. Instead, we suggest a (Bayesian) ordinal logistic model which corrects for the scoring error (compared with a gold standard) using a calibration data set.The marginal posterior distribution of the regression parameters of interest is obtained by integrating out the correction terms pertaining to the calibration data set. This is done by processing two Markov chains sequentially, whereby one Markov chain samples the correction terms. The sampled correction term is imputed in the Markov chain pertaining to the regression parameters. The model was fitted to the oral health data of the Signal-Tandmobiel ® study. A WinBUGS program was written to perform the analysis.

Annual Review of Public Health, 1995

This article reviews the Bayesian statistical approach to the design and analysis of research studies in the health sciences. The central idea of the Bayesian method is the use of study data to update the state of knowledge about a quantity of interest. In study design, the Bayesian approach explicitly incorporates expressions for the loss resulting from an incorrect decision at the end of the study. The Bayesian method also provides a flexible framework for the monitoring of sequential clinical trials. We present several examples of Bayesian methods in practice including a study of disease progression in AIDS, a comparison of two therapies in a clinical trial, and a case-control study investigating the link between dietary factors and breast cancer. 23 0163 -7525/95/0510-0023 $05.00

Cadernos De Saude Publica, 2014

2013 marked the 250th anniversary of the presentation of Bayes' theorem by the philosopher Richard Price. Thomas Bayes was a figure little known in his own time, but in the 20th century the theorem that bears his name became widely used in many fields of research. The Bayes theorem is the basis of the so-called Bayesian methods, an approach to statistical inference that allows studies to incorporate prior knowledge about relevant data characteristics into statistical analysis. Nowadays, Bayesian methods are widely used in many different areas such as astronomy, economics, marketing, genetics, bioinformatics and social sciences. This study observed that a number of authors discussed recent advances in techniques and the advantages of Bayesian methods for the analysis of epidemiological data. This article presents an overview of Bayesian methods, their application to epidemiological research and the main areas of epidemiology which should benefit from the use of Bayesian methods in coming years.

The Romanian Statistical Review, 2012

Purpose: To analysis the dependence of oral health diseases i.e. dental caries and periodontal disease on considering the number of risk factors through the applications of logistic regression model. Method: The cross sectional study involves a systematic random sample of 1760 permanent dentition aged between 18-40 years in Dharwad, Karnataka, India. Dharwad is situated in North Karnataka. The mean age was 34.26±7.28. The risk factors of dental caries and periodontal disease were established by multiple logistic regression model using SPSS statistical software. Results: The factors like frequency of brushing, timings of cleaning teeth and type of toothpastes are significant persistent predictors of dental caries and periodontal disease. The log likelihood value of full model is –1013.1364 and Akaike’s Information Criterion (AIC) is 1.1752 as compared to reduced regression model are -1019.8106 and 1.1748 respectively for dental caries. But, the log likelihood value of full model is –...

Cadernos de Saúde Pública, 2014

2013 marked the 250th anniversary of the presentation of Bayes’ theorem by the philosopher Richard Price. Thomas Bayes was a figure little known in his own time, but in the 20th century the theorem that bears his name became widely used in many fields of research. The Bayes theorem is the basis of the so-called Bayesian methods, an approach to statistical inference that allows studies to incorporate prior knowledge about relevant data characteristics into statistical analysis. Nowadays, Bayesian methods are widely used in many different areas such as astronomy, economics, marketing, genetics, bioinformatics and social sciences. This study observed that a number of authors discussed recent advances in techniques and the advantages of Bayesian methods for the analysis of epidemiological data. This article presents an overview of Bayesian methods, their application to epidemiological research and the main areas of epidemiology which should benefit from the use of Bayesian methods in co...