Bias and efficiency loss due to misclassified responses in binary regression
Bias and efficiency loss due to misclassified responses in binary regression
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DOI：
10.1093/biomet/86.4.843
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发表时间：
1999-12-01
期刊：
影响因子：
2.7
通讯作者：
Neuhaus, JM
中科院分区：
文献类型：
--
作者：
Neuhaus, JM
忽略二进制响应中的误差的方法产生协变量与响应的关联的有偏估计。本文推导出一般表达式的偏差的大小，由于在响应中的错误，并表明，除非灵敏度和特异性都非常高，忽略响应中的错误将产生高度偏倚的协变量效应估计。当真实的无误差响应遵循广义线性模型并且误分类概率已知且独立于协变量值时，观察到的有误差响应也遵循具有修改的链接函数的此类模型。我们描述了一个简单的方法来获得一致的估计协变量的影响和相关的错误，在这种情况下，并推导出一个表达式的协变量的影响估计的渐近相对效率从正确的似然响应的错误估计的基础上的真实，无错误的响应。此表达式表明响应中的错误可能导致有关协变量效应的大量信息丢失。一项关于妇女感染人乳头瘤病毒的研究和模拟研究的数据激发了这项工作，并说明了研究结果。
Methods that ignore errors in binary responses yield biased estimators of the associations of covariates with response. This paper derives general expressions for the magnitude of the bias due to errors in the response and shows that, unless both the sensitivity and specificity are very high, ignoring errors in the responses will yield highly biased covariate effect estimators. When the true, error-free response follows a generalised linear model and misclassification probabilities are known and independent of covariate values, responses observed with error also follow such a model with a modified link function. We describe a simple method to obtain consistent estimators of covariate effects and associated errors in this case, and derive an expression for the asymptotic relative efficiency of covariate effect estimators from the correct likelihood for the responses with errors with respect to estimates based on the true, error-free responses. This expression shows that errors in the response can lead to substantial losses of information about covariate effects. Data from a study on infection with human papilloma virus among women and simulation studies motivate this work and illustrate the findings.