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Related Experiment Video

Updated: Jun 27, 2026

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

Dealing with hierarchical data in periodontal research.

Hans-Peter Müller1

  • 1Institute of Clinical Dentistry (IKO), Faculty of Medicine, Tromsø University, 9037 Breivika, Norway. hans-peter.muller@fagmed.uit.no

Clinical Oral Investigations
|December 17, 2008
PubMed
Summary

This study introduces a multilevel model to analyze complex periodontal data, preserving valuable information lost in traditional methods. This approach offers unbiased estimates and deeper insights into subject and tooth variations for better clinical interpretation.

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Last Updated: Jun 27, 2026

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

Area of Science:

  • Periodontology
  • Biostatistics

Background:

  • Site-specific clinical periodontal data are rich but often hierarchical.
  • Summarizing data at a subject level for standard statistical tests results in significant information loss and potential interpretation fallacies.

Purpose of the Study:

  • To present a tutorial on applying a complex multilevel model for analyzing gingival thickness data.
  • To demonstrate how multilevel modeling can provide unbiased estimates and retain detailed data structure information.

Main Methods:

  • Application of a multilevel model to hierarchical periodontal data, specifically gingival thickness.
  • Focus on providing unbiased estimates of fixed effects and a detailed variance/covariance matrix.

Main Results:

  • The multilevel model effectively handles the hierarchical nature of periodontal data.
  • Unbiased estimates of fixed effects and a comprehensive variance/covariance matrix were obtained, reflecting data structure.

Conclusions:

  • Multilevel modeling is a powerful tool for analyzing complex periodontal data, overcoming limitations of traditional statistical methods.
  • Proper reporting of random effects in multilevel models can reveal crucial insights into data variation and model adequacy.