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A SEMI-PARAMETRIC BAYESIAN MODEL OF INTER- AND INTRA-EXAMINER AGREEMENT FOR PERIODONTAL PROBING DEPTH
1Department of Public Health Sciences Medical University Of South Carolina Hollings Cancer Center 86 Jonathan Lucas Street Suite 118 MSC 955 Charleston, South Carolina 29425-9550 USA hille@musc.edu.
The Annals of Applied Statistics
|September 30, 2014
Summary
This study introduces a Bayesian model to accurately measure periodontal probing depth, accounting for examiner bias and variability. The model improves precision in assessing periodontitis severity and examiner agreement.
Area of Science:
- Dentistry
- Biostatistics
- Periodontology
Background:
- Periodontal probing depth is crucial for assessing periodontitis severity.
- Accurate measurement is essential for reliable diagnosis and treatment monitoring.
- Existing methods may not fully account for measurement variability and bias.
Purpose of the Study:
- To develop a Bayesian hierarchical model for periodontal probing depth.
- To link true pocket depth with observed and recorded measurements.
- To account for correlations in measurements within mouths and between examiners.
Main Methods:
- A Bayesian hierarchical model was developed.
- The model incorporates periodontal site-specific examiner effects using a Dirichlet process mixture.
- Simulated data were used to evaluate the model's performance.
Main Results:
- The model successfully recovered examiner site-specific bias and variance heterogeneity.
- It provided cluster-adjusted point and interval agreement estimates.
- The model demonstrated utility in analyzing calibration training data.
Conclusions:
- The developed Bayesian model enhances the accuracy of periodontal probing depth assessment.
- It offers a robust framework for understanding and correcting measurement bias.
- This approach can improve the reliability of periodontitis severity evaluations.
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