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Related Experiment Videos

Bayesian analysis of clustered interval-censored data.

M C M Wong1, K F Lam, E C M Lo

  • 1Dental Public Health, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China. mcmwong@hkucc.hku.hk

Journal of Dental Research
|August 20, 2005
PubMed
Summary

The Bayesian approach effectively modeled multilevel dental data. Silver diamine fluoride application shortened dentin caries arrest time in Chinese preschoolers.

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Area of Science:

  • Dentistry
  • Biostatistics
  • Pediatric Oral Health

Background:

  • Interval-censored failure times are frequent in dental research, posing modeling challenges.
  • Multilevel data analysis is complex, especially with clustered observations like multiple caries lesions per child.

Purpose of the Study:

  • To investigate the effectiveness of silver diamine fluoride (SDF) and sodium fluoride (NaF) varnish in arresting dentin caries in Chinese preschool children.
  • To analyze multilevel clustered interval-censored data using a Bayesian approach.

Main Methods:

  • A three-level random-effects Weibull regression model was employed.
  • Bayesian analysis was conducted using WinBUGS software.
  • Data comprised time-to-arrest measurements for dentin caries on various tooth surfaces within the same child.

Main Results:

  • A significant positive correlation (0.596) was found among caries arrest times on different surfaces of the same child, indicating clustering effects.
  • The Bayesian approach using WinBUGS simplified the complex estimation process.

Conclusions:

  • Annual application of silver diamine fluoride (SDF) on caries lesions is effective in shortening the time to arrest.
  • Caries removal prior to SDF application further reduces the arrest time.
  • The Bayesian method proved efficient for analyzing complex multilevel dental data.

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