College of Nursing, Rutgers University, Newark, New Jersey 07102, USA.
Nurs Res. 2012 May-Jun;61(3):188-94. doi: 10.1097/NNR.0b013e31824f5f58.
Researchers often describe the collection of repeated measurements on each individual in a study design. Advanced statistical methods, namely, mixed and marginal models, are the preferred analytic choices for analyzing this type of data.
The aim was to provide a conceptual understanding of these modeling techniques.
An understanding of mixed models and marginal models is provided via a thorough exploration of the methods that have been used historically in the biomedical literature to summarize and make inferences about this type of data. The limitations are discussed, as is work done on expanding the classic linear regression model to account for repeated measurements taken on an individual, leading to the broader mixed-model framework.
A description is provided of a variety of common types of study designs and data structures that can be analyzed using a mixed model and a marginal model.
This work provides an overview of advanced statistical modeling techniques used for analyzing the many types of correlated .data collected in a research study.
研究人员经常描述在研究设计中对每个个体进行重复测量的收集。高级统计方法，即混合和边缘模型，是分析此类数据的首选分析选择。
旨在提供对这些建模技术的概念理解。
通过深入探讨生物医学文献中历史上用于总结和推断此类数据的方法，提供了对混合模型和边缘模型的理解。讨论了局限性，以及为了说明对个体进行重复测量而对经典线性回归模型进行扩展所做的工作，从而形成了更广泛的混合模型框架。
提供了使用混合模型和边缘模型分析各种常见类型的研究设计和数据结构的描述。
这项工作概述了用于分析研究中收集的多种相关数据的高级统计建模技术。
