Marginal modeling of nonnested multilevel data using standard software
Marginal modeling of nonnested multilevel data using standard software
复制标题
DOI：
10.1093/aje/kwk020
复制
发表时间：
2007-02-15
影响因子：
5
通讯作者：
Heagerty, Patrick J.
中科院分区：
文献类型：
--
作者：
Miglioretti, Diana L.;Heagerty, Patrick J.
流行病学数据通常聚集在多个级别中，这些级别可能不会相互嵌套。在拟合回归模型时，广义估计方程通常用于调整聚类内观测值之间的相关性；然而，标准软件目前不支持非嵌套集群。本文介绍了一种简单的广义估计方程策略，该策略使用可用的商业或公共软件对非嵌套多级数据进行回归分析。作者描述了如何获得经验标准误差估计，以构建有效的置信区间和进行统计假设检验。该方法通过模拟进行评估，并通过对乳腺癌监测联盟的数据分析进行说明，该联盟估计女性、放射科医生和设施特征对筛查乳房 X 光检查的阳性预测值的影响。讨论了少量集群的性能。模拟和示例都证明了考虑所有级别的聚类内的相关性以进行正确推理的重要性。
Epidemiologic data are often clustered within multiple levels that may not be nested within each other. Generalized estimating equations are commonly used to adjust for correlation among observations within clusters when fitting regression models; however, standard software does not currently accommodate nonnested clusters. This paper introduces a simple generalized estimating equation strategy that uses available commercial or public software for the regression analysis of nonnested multilevel data. The authors describe how to obtain empirical standard error estimates for constructing valid confidence intervals and conducting statistical hypothesis tests. The method is evaluated using simulations and illustrated with an analysis of data from the Breast Cancer Surveillance Consortium that estimates the influence of woman, radiologist, and facility characteristics on the positive predictive value of screening mammography. Performance with a small number of clusters is discussed. Both the simulations and the example demonstrate the importance of accounting for the correlation within all levels of clustering for proper inference.