Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran.
Department of Biostatistics, Infertility Research Center, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran.
Comput Math Methods Med. 2013;2013:170120. doi: 10.1155/2013/170120. Epub 2013 Dec 23.
Misclassification of exposure variables in epidemiologic studies may lead to biased estimation of parameters and loss of power in statistical inferences. In this paper, the inverse matrix method, as an efficient method of the correction of odds ratio for the misclassification of a binary exposure, was generalized to nondifferential misclassification and 2 × 2 × J tables.
Simple estimates for predictive values when misclassification is nondifferential are presented. Using them, we estimated the corrected log odds ratio and its variance for 2 × 2 × J tables, using the inverse matrix method. A two-step weighted likelihood method was also developed. Moreover, we compared the matrix and inverse matrix methods to the maximum likelihood (MLE) method using a simulation study.
In all situations, the inverse matrix method proved to be more efficient than the matrix method. Matrix and inverse matrix methods for nondifferential situations are more efficient than differential misclassification.
Although MLE is optimal among all of the methods, it is computationally difficult and requires programming. On the other hand, the inverse matrix method with a simple closed-form presents acceptable efficiency.
在流行病学研究中，暴露变量的分类错误可能导致参数估计偏倚和统计推断的效能损失。本文将用于校正二分类暴露错误的比值比的逆矩阵方法推广到非差异分类和 2×2×J 表。
本文提出了非差异分类情况下预测值的简单估计。利用这些估计值，我们使用逆矩阵方法估计了 2×2×J 表的校正对数比值比及其方差。此外，我们还使用模拟研究比较了矩阵和逆矩阵方法与最大似然（MLE）方法。
在所有情况下，逆矩阵方法均证明比矩阵方法更有效。非差异情况下的矩阵和逆矩阵方法比差异分类更有效。
虽然 MLE 是所有方法中最优的，但它在计算上较为困难，需要编程。另一方面，具有简单闭式的逆矩阵方法具有可接受的效率。
