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Related Experiment Video

Updated: Jun 30, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
07:32

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Multivariate logit copula model with an application to dental data.

Aristidis K Nikoloulopoulos1, Dimitris Karlis

  • 1Department of Statistics, Athens University of Economics and Business, 76 Patission Str., 10434 Athens, Greece.

Statistics in Medicine
|September 26, 2008
PubMed
Summary
This summary is machine-generated.

This study models multivariate binary data using copulas, revealing that fluoride and age influence dental caries associations. These findings highlight the importance of considering covariate effects on dependence structures in binary data analysis.

Related Experiment Videos

Last Updated: Jun 30, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
07:32

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Area of Science:

  • Statistics
  • Biostatistics
  • Multivariate Data Analysis

Background:

  • Copula applications are common for continuous data but less explored for multivariate binary data.
  • Marginal distributions impact dependence measures when applying copulas to binary data.
  • Existing methods often overlook direct covariate effects on dependence structures in binary outcomes.

Purpose of the Study:

  • To model multivariate binary data using mixtures of max-infinitely divisible copulas.
  • To incorporate covariate information directly into copula parameters to assess their effect on dependence.
  • To address model uncertainty using a model averaging technique.

Main Methods:

  • Utilized mixtures of max-infinitely divisible copulas for modeling multivariate binary data.
  • Incorporated covariate information (fluoride, age) into copula parameters to analyze dependence.
  • Employed a model averaging technique to handle model uncertainty.
  • Applied the model to binary caries experience data from the Signal-Tandmobiel dental study.

Main Results:

  • Identified systematically larger associations between mandibular molars and between maxillary molars.
  • Demonstrated that covariates like fluoride and age significantly affect these molar associations.
  • Revealed dependence structures not detectable by marginal model-focused methods.

Conclusions:

  • Copula models incorporating covariate information effectively capture dependence structures in multivariate binary data.
  • Fluoride and age are significant factors influencing caries experience associations among molars.
  • This approach offers deeper insights into complex relationships in binary outcomes compared to marginal analyses.