Generalized estimating equations for ordinal categorical data: Arbitrary patterns of missing responses and missingness in a key covariate
Generalized estimating equations for ordinal categorical data: Arbitrary patterns of missing responses and missingness in a key covariate
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DOI：
10.1111/j.0006-341x.1999.00488.x
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发表时间：
1999-06-01
期刊：
影响因子：
1.9
通讯作者：
Gatsonis, C
中科院分区：
文献类型：
--
作者：
Toledano, AY;Gatsonis, C
我们提出的方法，重复测量的有序分类数据的回归分析时，有非单调的缺失，在这些反应，当一个关键的协变量是失踪的，这取决于可观的。该方法使用有序回归模型结合广义估计方程（GEE）。我们扩展的GEE方法，以适应任意模式的缺失的响应时，这种缺失是独立的未观察到的反应。我们进一步扩展的方法，以提供可能的偏差时，一个关键的协变量的知识缺失可能取决于可观测值的校正。该方法说明了诊断肿瘤学的研究，其中多个相关的接收器工作特性曲线估计和校正可能的验证偏差时，真正的疾病状态是失踪的，这取决于观察数据的数据分析。
We propose methods for regression analysis of repeatedly measured ordinal categorical data when there is nonmonotone missingness in these responses and when a key covariate is missing depending on observables. The methods use ordinal regression models in conjunction with generalized estimating equations (GEEs). We extend the GEE methodology to accommodate arbitrary patterns of missingness in the responses when this missingness is independent of the unobserved responses. We further extend the methodology to provide correction for possible bias when missingness in knowledge of a key covariate may depend on observables. The approach is illustrated with the analysis of data from a study in diagnostic oncology in which multiple correlated receiver operating characteristic Curves are estimated and corrected for possible verification bias when the true disease status is missing depending on observables.