From the ICES (P.C.A., M.K.K., M.V.V., J.F., A.Y.X.Y.), Toronto; Institute of Health Policy, Management and Evaluation (P.C.A., M.K.K., M.V.V., A.Y.X.Y.), University of Toronto; Sunnybrook Research Institute (P.C.A., A.Y.X.Y.), Toronto; Division of General Internal Medicine (M.K.K.), and Division of Neurology (M.V.V., A.Y.X.Y.), Department of Medicine, University of Toronto, Ontario, Canada.
Neurology. 2024 Nov 12;103(9):e209947. doi: 10.1212/WNL.0000000000209947. Epub 2024 Oct 11.
In clinical and health services research, clustered data (also known as data with a multilevel or hierarchical structure) are frequently encountered. For example, patients may be clustered or nested within hospitals. Understanding when data have a multilevel structure is important because clustering of individuals can induce a homogeneity in outcomes within clusters, so that, even after adjusting for measured covariates, outcomes for 2 individuals in the same cluster are more likely to be similar than outcomes for 2 individuals from different clusters. Using conventional statistical regression models to analyze clustered data can result in incorrect conclusions being drawn. In particular, estimated CIs may be artificially narrow, and significance levels may be artificially low. As a result, one may conclude that there is a statistically significant association when there is none. To avoid this problem, investigators should ensure that their analyses use techniques that account for clustering of data. Generalized linear models estimated using generalized estimating equation (GEE) methods and multilevel regression models (also known as hierarchical regression models, mixed-effects models, or random-effects models) are two such techniques. We provide an introduction to clustered or multilevel data and describe how GEE models or multilevel models can be used for the analysis of multilevel data.
在临床和卫生服务研究中，经常会遇到聚类数据（也称为具有多层次或层次结构的数据）。例如，患者可能会按医院聚类或嵌套。了解数据是否具有多层次结构很重要，因为个体的聚类会导致聚类内结果的同质性，因此，即使在调整了测量协变量后，同一聚类中的 2 个个体的结果也更有可能相似，而不是来自不同聚类的 2 个个体的结果。使用传统的统计回归模型分析聚类数据可能会导致得出错误的结论。特别是，估计的置信区间可能人为地变窄，显著性水平可能人为地降低。因此，人们可能会得出一种统计学上显著的关联，而实际上并没有。为了避免这个问题，研究人员应该确保他们的分析使用考虑数据聚类的技术。使用广义估计方程（GEE）方法估计的广义线性模型和多层次回归模型（也称为层次回归模型、混合效应模型或随机效应模型）就是两种这样的技术。我们将介绍聚类或多层次数据，并描述如何使用 GEE 模型或多层次模型来分析多层次数据。
