J Periodontal Implant Sci.  2015 Feb;45(1):2-7. 10.5051/jpis.2015.45.1.2.

Analysis of periodontal data using mixed effects models

• Cho YI
• Kim HY

• KMID: 2027826
• DOI: http://doi.org/10.5051/jpis.2015.45.1.2

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.

Keyword

Linear models; Statistics; Statistical data interpretation

MeSH Terms

Bias (Epidemiology)
Data Interpretation, Statistical
Dental Research
Least-Squares Analysis
Linear Models
Models, Statistical

Figure

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  Figure 1 The complex multilevel structure of a periodontal data.

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  Figure 2 Depiction of repeated measurements (A) at multiple sites in a single individual (person level) and (B) in multiple individuals in a community (community level).

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