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Related Experiment Video

Updated: Jul 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

The zero-inflated negative binomial regression model with correction for misclassification: an example in caries

Samuel M Mwalili1, Emmanuel Lesaffre, Dominique Declerck

  • 1Statistics and Actuarial Sciences, Jorno Kenyatta University of Agriculture and Technology, Kenya.

Statistical Methods in Medical Research
|August 19, 2007
PubMed
Summary

This study introduces a novel method to correct misclassified count data in complex zero-inflated negative binomial models. The approach addresses measurement error in dental caries research, improving data accuracy.

Related Experiment Videos

Last Updated: Jul 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Statistics
  • Biostatistics
  • Dental Research

Background:

  • Zero-inflated models are widely used for count data across various disciplines.
  • Count data are susceptible to measurement error, specifically misclassification.
  • Existing methods for count misclassification are limited to binomial and Poisson models.

Purpose of the Study:

  • To extend methods for correcting count misclassification to the zero-inflated negative binomial (ZINB) model.
  • To address misclassification in the context of dental caries experience measurement (dmft-index).
  • To investigate the impact of multiple examiners on misclassification patterns.

Main Methods:

  • Development of a statistical framework for misclassification correction within ZINB models.
  • Application of the method to the dmft-index, a measure of caries experience.
  • Analysis of misclassification introduced by multiple dental examiners.

Main Results:

  • Successfully demonstrated a method to correct for misclassification in ZINB models.
  • Illustrated the application using dmft-index data from caries research.
  • Showed how non-differential misclassification by individual examiners can result in differential misclassification overall.

Conclusions:

  • The proposed method effectively corrects for misclassification in ZINB models.
  • The approach is valuable for accurately analyzing count data prone to measurement error, particularly in dental research.
  • Understanding examiner-specific misclassification is crucial for accurate epidemiological studies.