Analysis of periodontal data using mixed effects models
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A fundamental problem in analyzing complex multilevel-structured periodontal data is theviolation of independency among the observations, which is an assumption in traditionalstatistical models (e.g., analysis of variance and ordinary least squares regression). In manycases, 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 powerand detailed information, no cross-level relationship analysis, and the potential for creatingan ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effectsmodels have been introduced and employed in dental research using periodontaldata. The use of mixed effects models might account for the potential bias due to the violationof the independency assumption as well as provide accurate estimates.
Abstract
A fundamental problem in analyzing complex multilevel-structured periodontal data is theviolation of independency among the observations, which is an assumption in traditionalstatistical models (e.g., analysis of variance and ordinary least squares regression). In manycases, 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 powerand detailed information, no cross-level relationship analysis, and the potential for creatingan ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effectsmodels have been introduced and employed in dental research using periodontaldata. The use of mixed effects models might account for the potential bias due to the violationof the independency assumption as well as provide accurate estimates.
참고문헌 (21)
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