Espeland M A, Hui S L
Center for Prevention Research and Biometry, Bowman Gray School of Medicine, Winston-Salem, North Carolina 27103.
Biometrics. 1987 Dec;43(4):1001-12.
Misclassification is a common source of bias and reduced efficiency in the analysis of discrete data. Several methods have been proposed to adjust for misclassification using information on error rates (i) gathered by resampling the study population, (ii) gathered by sampling a separate population, or (iii) assumed a priori. We present unified methods for incorporating these types of information into analyses based on log-linear models and maximum likelihood estimation. General variance expressions are developed. Examples from epidemiologic studies are used to demonstrate the proposed methodology.
错误分类是离散数据分析中偏差和效率降低的常见来源。已经提出了几种方法，用于使用以下信息调整错误分类：(i) 通过对研究人群进行重采样收集的错误率信息；(ii) 通过对单独人群进行采样收集的错误率信息；或(iii) 先验假设的错误率信息。我们提出了统一的方法，将这些类型的信息纳入基于对数线性模型和最大似然估计的分析中。推导了一般方差表达式。使用流行病学研究的例子来证明所提出的方法。
