Open AccessUse of the generalized linear models in data related to dental caries index
Author(s) -
Shivalingappa B Javali,
Parameshwar V. Pandit
Publication year - 2007
Publication title -
indian journal of dental research/indian journal of dental research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 43
eISSN - 1998-3603
pISSN - 0970-9290
DOI - 10.4103/0970-9290.35825
Subject(s) - covariate , poisson distribution , probit model , statistics , probit , negative binomial distribution , logit , generalized linear model , poisson regression , mathematics , logistic regression , count data , index (typography) , overdispersion , binomial (polynomial) , econometrics , medicine , computer science , population , environmental health , world wide web
The aim of this study is to encourage and initiate the application of generalized linear models (GLMs) in the analysis of the covariates of decayed, missing, and filled teeth (DMFT) index data, which is not necessarily normally distributed. GLMs can be performed assuming underlying many distributions; in fact Poisson distribution with log built-in link function and binomial distribution with Logit and Probit built-in link functions are considered. The Poisson model is used for modeling the DMFT index data and the Logit and Probit models are employed to model the dichotomous outcome of DMFT = 0 and DMFT not equal to 0 (caries free/caries present). The data comprised 7188 subjects aged 18-30 years from the study on the oral health status of Karnataka state conducted by SDM College of Dental Sciences and Hospital, Dharwad, Karnataka, India. The Poisson model and binomial models (Logit and Probit) displayed dissimilarity in the outcome of results at 5% level of significance ( P <0.05). The binomial models were a poor fit, whereas the Poisson model showed a good fit for the DMFT index data. Therefore, a suitable modeling approach for DMFT index data is to use a Poisson model for the DMFT response and a binomial model for the caries free and caries present (DMFT = 0 and DMFT not equal to 0). These GLMs allow separate estimation of those covariates which influence the magnitude of caries.