Analysis of periodontal data using mixed effects models

Young Il Cho, Hae-Young Kim

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.

Original languageEnglish
Pages (from-to)2-7
Number of pages6
JournalJournal of Periodontal and Implant Science
Volume45
Issue number1
DOIs
Publication statusPublished - 2015

Fingerprint

Dental Research
Statistical Models
Least-Squares Analysis
Analysis of Variance

Keywords

  • Linear models
  • Statistical data interpretation
  • Statistics

ASJC Scopus subject areas

  • Oral Surgery
  • Periodontics

Cite this

Analysis of periodontal data using mixed effects models. / Cho, Young Il; Kim, Hae-Young.

In: Journal of Periodontal and Implant Science, Vol. 45, No. 1, 2015, p. 2-7.

Research output: Contribution to journalArticle

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