Marginal modeling of multilevel binary data with time-varying covariates.
Marginal modeling of multilevel binary data with time-varying covariates.
复制标题
具有时变协变量的多级二进制数据的边际建模。
DOI：
10.1093/biostatistics/5.3.381
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Heagerty,PatrickJ
中科院分区：
文献类型：
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作者：
Miglioretti,DianaL;Heagerty,PatrickJ
当聚类不一定嵌套时，我们提出并比较了两种用于多水平二进制数据回归分析的方法：GEE方法依赖于工作独立性假设，再加上三步法获得经验标准误差，以及使用贝叶斯计算技术实现的基于似然的方法。讨论了随时间变化的内源性协变量的含义。该方法说明使用数据从乳腺癌监测联盟估计乳房X光检查的准确性，从一个重复筛选的人口。
We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three‐step method for obtaining empirical standard errors, and a likelihood‐based method implemented using Bayesian computational techniques. Implications of time‐varying endogenous covariates are addressed. The methods are illustrated using data from the Breast Cancer Surveillance Consortium to estimate mammography accuracy from a repeatedly screened population.