Analysis of periodontal data using mixed effects models.
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.
Department of Psychology, Sungshin Women's University, Seoul, Korea.
Young Il
0000-0003-3235-7613
YI
Cho
Department of Health Policy Management, College of Health Science & Department of Public Health Sciences, Graduate School, Korea University, Seoul, Korea.
Hae-Young
0000-0003-2043-2575
HY
Kim
Linear models; Statistical data interpretation; Statistics