Müller Hans-Peter
Institute of Clinical Dentistry (IKO), Faculty of Medicine, Tromsø University, 9037 Breivika, Norway.
Clin Oral Investig. 2009 Sep;13(3):273-8. doi: 10.1007/s00784-008-0237-1. Epub 2008 Dec 16.
Site-specific clinical periodontal data are usually plentiful, typically hierarchical, and generally valuable information. Summarizing these data on a subject level for easy application of standard statistical tests leads to loss of most of the information. In addition, well-known fallacies may make interpretation difficult if not impossible. In this study, an attempt is made to apply, in a non-technical way and as a tutorial, a rather complex multilevel model of gingival thickness, which provides unbiased estimates of fixed effects and a variance/covariance matrix with considerable information as regards data structure. When applying multilevel modeling, random effects should generally be reported in a proper way, since they might reveal new insights into subject and tooth variation, correlations between covariates, and even problems with the chosen model.
特定部位的临床牙周数据通常丰富，典型地具有层次性，并且一般是有价值的信息。在个体水平上汇总这些数据以便于应用标准统计检验会导致大部分信息丢失。此外，一些众所周知的谬误可能使解释变得困难甚至无法解释。在本研究中，尝试以一种非技术性的方式并作为一个教程应用一个相当复杂的牙龈厚度多层次模型，该模型可提供固定效应的无偏估计以及一个关于数据结构具有大量信息的方差/协方差矩阵。应用多层次建模时，通常应以适当方式报告随机效应，因为它们可能揭示关于个体和牙齿变异、协变量之间的相关性甚至所选模型存在的问题的新见解。
