Exact confidence limits for prevalence of a disease with an imperfect diagnostic test
Exact confidence limits for prevalence of a disease with an imperfect diagnostic test
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DOI：
10.1017/s0950268810000385
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发表时间：
2010-11-01
影响因子：
4.2
通讯作者：
Ozsvari, L.
中科院分区：
文献类型：
--
作者：
Reiczigel, J.;Foldi, J.;Ozsvari, L.
疾病流行率的估计，包括置信区间的构建，在筛查调查以及疾病状况监测中至关重要。在大多数调查数据的分析中，隐含地假设诊断测试具有100%的灵敏度和特异性。然而，这种假设在大多数情况下是无效的。此外，使用正态分布作为真实抽样分布近似值的渐近方法可能无法保持所需的标称置信水平。在这里，我们提出了疾病患病率的精确双侧置信区间，考虑到诊断测试的灵敏度和特异性。我们说明了广泛的模拟研究和现实生活中的例子的结果的方法的优势。
Estimation of prevalence of disease, including construction of confidence intervals, is essential in surveys for screening as well as in monitoring disease status. In most analyses of survey data it is implicitly assumed that the diagnostic test has a sensitivity and specificity of 100%. However, this assumption is invalid in most cases. Furthermore, asymptotic methods using the normal distribution as an approximation of the true sampling distribution may not preserve the desired nominal confidence level. Here we proposed exact two-sided confidence intervals for the prevalence of disease, taking into account sensitivity and specificity of the diagnostic test. We illustrated the advantage of the methods with results of an extensive simulation study and real-life examples.