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Discrete Distribution Based on Compound Sum to Model Dental Caries Count Data.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Dental Public Health

Background:

  • Dental caries analysis methods have advanced, with increasing use of zero-inflated or hurdle models for decayed, missing, and filled teeth (DMFT) data.
  • Existing models struggle with DMFT distribution's skewness and high zero scores.
  • There is a need for statistical models incorporating biological factors for dental caries in surveys.

Purpose of the Study:

  • To present the generalized negative binomial (GNB) distribution as a novel statistical model for dental caries count data.
  • To demonstrate the GNB model's applicability using data from the EPIPAP study.
  • To compare the GNB model with existing zero-inflated and hurdle models.

Main Methods:

  • Expressed zero-inflated and hurdle models as a compound sum.
  • Introduced the generalized negative binomial (GNB) distribution using the same compound sum framework.
  • Applied the GNB model to dental caries count data from the EPIPAP study.

Main Results:

  • The GNB model was successfully applied to dental caries count data.
  • The GNB model demonstrated superior performance in generating score functions.
  • The GNB model handles the lifetime dental caries disease process more effectively than traditional models.

Conclusions:

  • The GNB distribution is well-suited for modeling dental caries count data, especially when structural zeros are infrequent.
  • The GNB model accommodates scenarios where multiple underlying factors contribute to new disease events.
  • The GNB distribution offers a relevant and improved approach for epidemiological surveys of dental caries.