Magder L S, Hughes J P
Department of Epidemiology and Preventive Medicine, University of Maryland at Baltimore, USA.
Am J Epidemiol. 1997 Jul 15;146(2):195-203. doi: 10.1093/oxfordjournals.aje.a009251.
In epidemiologic research, logistic regression is often used to estimate the odds of some outcome of interest as a function of predictors. However, in some datasets, the outcome of interest is measured with imperfect sensitivity and specificity. It is well known that the misclassification induced by such an imperfect diagnostic test will lead to biased estimates of the odds ratios and their variances. In this paper, the authors show that when the sensitivity and specificity of a diagnostic test are known, it is straightforward to incorporate this information into the fitting of logistic regression models. An EM algorithm that produces unbiased estimates of the odds ratios and their variances is described. The resulting odds ratio estimates tend to be farther from the null but have greater variance than estimates found by ignoring the imperfections of the test. The method can be extended to the situation where the sensitivity and specificity differ for different study subjects, i.e., nondifferential misclassification. The method is useful even when the sensitivity and specificity are not known, as a way to see the degree to which various assumptions about sensitivity and specificity affect one's estimates. The method can also be used to estimate sensitivity and specificity under certain assumptions or when a validation subsample is available. Several examples are provided to compare the results of this method with those obtained by standard logistic regression. A SAS macro that implements the method is available on the World Wide Web at http:@som1.ab.umd.edu/Epidemiology/software.h tml.
在流行病学研究中，逻辑回归常用于估计作为预测因素函数的某些感兴趣结局的比值比。然而，在一些数据集中，感兴趣的结局是以不完美的灵敏度和特异度来测量的。众所周知，这种不完美诊断测试引起的错误分类将导致比值比及其方差的估计出现偏差。在本文中，作者表明，当诊断测试的灵敏度和特异度已知时，将此信息纳入逻辑回归模型的拟合过程很简单。本文描述了一种能产生比值比及其方差无偏估计的期望最大化（EM）算法。所得的比值比估计往往离零假设更远，但比忽略测试不完美性所得到的估计具有更大的方差。该方法可扩展到不同研究对象的灵敏度和特异度不同的情况，即非差异性错误分类。即使灵敏度和特异度未知，该方法也很有用，可作为一种了解关于灵敏度和特异度的各种假设对估计影响程度的方式。该方法还可用于在某些假设下或有验证子样本时估计灵敏度和特异度。提供了几个例子来比较该方法与标准逻辑回归所得结果。可在万维网http:@som1.ab.umd.edu/Epidemiology/software.html上获取实现该方法的SAS宏。
