Reade-Christopher S J, Kupper L L
Research Triangle Institute, North Carolina 27709.
Biometrics. 1991 Jun;47(2):535-48.
In epidemiologic studies, subjects are often misclassified as to their level of exposure. Ignoring this misclassification error in the analysis introduces bias in the estimates of certain parameters and invalidates many hypothesis tests. For situations in which there is misclassification of exposure in a follow-up study with categorical data, we have developed a model that permits consideration of any number of exposure categories and any number of multiple-category covariates. When used with logistic and Poisson regression procedures, this model helps assess the potential for bias when misclassification is ignored. When reliable ancillary information is available, the model can be used to correct for misclassification bias in the estimates produced by these regression procedures.
在流行病学研究中，研究对象的暴露水平常常被错误分类。在分析中忽略这种错误分类误差会在某些参数估计中引入偏差，并使许多假设检验无效。对于在具有分类数据的随访研究中存在暴露错误分类的情况，我们开发了一种模型，该模型允许考虑任意数量的暴露类别和任意数量的多类别协变量。当与逻辑回归和泊松回归程序一起使用时，该模型有助于评估忽略错误分类时偏差的可能性。当有可靠的辅助信息可用时，该模型可用于校正这些回归程序产生的估计中的错误分类偏差。
