Horton Nicholas J, Fitzmaurice Garrett M
Department of Mathematics, Smith College, College Lane, Northampton, MA 01063, USA.
Stat Med. 2004 Sep 30;23(18):2911-33. doi: 10.1002/sim.1879.
In this tutorial, we describe regression-based methods for analysing multiple source data arising from complex sample survey designs. We use the term 'multiple-source' data to encompass all cases where data are simultaneously obtained from multiple informants, or raters (e.g. self-reports, family members, health care providers, administrators) or via different/parallel instruments, indicators or methods (e.g. symptom rating scales, standardized diagnostic interviews, or clinical diagnoses). We review regression models for analysing multiple source risk factors or multiple source outcomes and show that they can be considered special cases of generalized linear models, albeit with correlated outcomes. We show how these methods can be extended to handle the common survey features of stratification, clustering, and sampling weights. We describe how to fit regression models with multiple source reports derived from complex sample surveys using general purpose statistical software. Finally, the methods are illustrated using data from two studies: the Stirling County Study and the Eastern Connecticut Child Survey.
在本教程中，我们描述了用于分析复杂样本调查设计中产生的多源数据的基于回归的方法。我们使用“多源”数据这一术语来涵盖所有从多个信息提供者或评估者（例如自我报告、家庭成员、医疗保健提供者、管理人员）同时获取数据的情况，或者通过不同/并行的工具、指标或方法（例如症状评定量表、标准化诊断访谈或临床诊断）获取数据的情况。我们回顾了用于分析多源风险因素或多源结果的回归模型，并表明它们可以被视为广义线性模型的特殊情况，尽管结果是相关的。我们展示了如何扩展这些方法以处理分层、聚类和抽样权重等常见调查特征。我们描述了如何使用通用统计软件对来自复杂样本调查的多源报告拟合回归模型。最后，使用两项研究的数据对这些方法进行了说明：斯特林县研究和东康涅狄格儿童调查。
