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Analysis of dental caries using generalized linear and count regression models

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Author Info

  • S. B. Javali M. Phil

    (USM-KLE International Medical School, Karnataka, India)

  • Parameshwar V. Pandit

    (Bangalore University)

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    Abstract

    Generalized linear models (GLM) are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are fl exible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero infl ated count regression models such as the zero-inflated Poisson (ZIP), zeroinflated negative binomial (ZINB) regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and overdispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.

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    File URL: http://www.revistadestatistica.ro/Articole/2013/RRS_10_2013_a4en.pdf
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    Bibliographic Info

    Article provided by Romanian Statistical Review in its journal Romanian Statistical Review.

    Volume (Year): 61 (2013)
    Issue (Month): 10 (November)
    Pages: 73-82

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    Handle: RePEc:rsr:journl:v:61:y:2013:i:10:p:73-82

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    Related research

    Keywords: GLM; Poisson; Negative Binomial; ZIP and ZINB regression models; over-dispersion; zero-inflation; DMFT Index data;

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