Asymptotic variance estimation for the misclassification SIMEX
Asymptotic variance estimation for the misclassification SIMEX
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DOI：
10.1016/j.csda.2006.12.045
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发表时间：
2007-08-15
影响因子：
1.8
通讯作者：
Lesaffre, Emmanuel
中科院分区：
文献类型：
--
作者：
Kuechenhoff, Helmut;Lederer, Wolfgang;Lesaffre, Emmanuel
大多数流行病学研究在响应和/或协变量方面存在分类错误。由于忽略误分类会导致参数估计值出现偏差，因此对此类错误进行校正非常重要。对于测量误差，即误分类的连续模拟，一般的偏差校正方法是SIMEX（模拟外推）方法。这种方法最近已扩展到回归模型可能被错误分类的分类响应和/或协变量，并被称为MC-SIMEX方法。为了评估回归量的重要性，不仅需要其（校正）估计值，还需要其标准误差。在原SIMEX方法的基础上，提出了一种利用渐近展开估计渐近方差的方法。推导了MC-SIMEX方法的渐近方差估计。当误分类概率估计的验证研究的情况下，也包括在内。一个广泛的仿真研究表明，新的方法的良好性能。它说明了一个例子，在龋齿研究，包括逻辑回归模型，其中的响应和二元协变量可能被错误分类。(C)2007 Elsevier B. V.保留所有权利。
Most epidemiological studies suffer from misclassification in the response and/or the covariates. Since ignoring misclassification induces bias on the parameter estimates, correction for such errors is important. For measurement error, the continuous analog to misclassification, a general approach for bias correction is the SIMEX (simulation extrapolation) method. This approach has been recently extended to regression models with a possibly misclassified categorical response and/or the covariates and is called the MC-SIMEX approach. In order to assess the importance of a regressor not only its (corrected) estimate is needed, but also its standard error. Based on the original SIMEX approach a method which uses asymptotic expansions to estimate the asymptotic variance is developed. The asymptotic variance estimators for the MC-SIMEX approach are derived. The case when the misclassification probabilities are estimated by a validation study is also included. An extensive simulation study shows the good performance of the new approach. It is illustrated by an example in caries research including a logistic regression model, where the response and a binary covariate are possibly misclassified. (C) 2007 Elsevier B.V. All rights reserved.