A Bayesian approach to strengthen inference for case-control studies with multiple error-prone exposure assessments.
A Bayesian approach to strengthen inference for case-control studies with multiple error-prone exposure assessments.
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DOI：
10.1002/sim.5842
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发表时间：
2013-11-10
影响因子：
2
通讯作者：
Chu, Haitao
中科院分区：
文献类型：
--
作者：
Zhang, Jing;Cole, Stephen R.;Richardson, David B.;Chu, Haitao
在病例对照研究中，暴露评估几乎总是容易出错。在没有黄金标准的情况下，经常使用两种或两种以上的评估方法来根据暴露情况对人进行分类。每一种不完善的评估工具都可能导致对暴露分配的错误分类；暴露的错误分类可能因案例状态而异或不同；不同方法下的暴露分类错误可能是独立的(取决于真实的暴露状态)或不独立。虽然已经提出了在缺乏金标准的情况下研究诊断准确性的方法，但这些方法很少用于病例对照研究，以纠正同时具有差异性和相关性的暴露错误分类。在这篇文章中，我们提出了一种贝叶斯方法来估计测量误差校正的暴露-疾病关联，同时考虑了差异和相关的错误分类。通过模拟研究了该方法的性能，结果表明该方法效果良好，并应用于一项评估石棉暴露与间皮瘤相关性的病例对照研究。
In case-control studies, exposure assessments are almost always error-prone. In the absence of a gold standard, two or more assessment approaches are often used to classify people with respect to exposure. Each imperfect assessment tool may lead to misclassification of exposure assignment; the exposure misclassification may be differential with respect to case status or not; and, the errors in exposure classification under the different approaches may be independent (conditional upon the true exposure status) or not. Although methods have been proposed to study diagnostic accuracy in the absence of a gold standard, these methods are infrequently used in case-control studies to correct exposure misclassification that is simultaneously differential and dependent. In this paper, we proposed a Bayesian method to estimate the measurement-error corrected exposure-disease association, accounting for both differential and dependent misclassification. The performance of the proposed method is investigated using simulations, which show that the proposed approach works well, as well as an application to a case-control study assessing the association between asbestos exposure and mesothelioma.
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