On the interpretation of test sensitivity in the two-test two-population problem: Assumptions matter
On the interpretation of test sensitivity in the two-test two-population problem: Assumptions matter
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DOI：
10.1016/j.prevetmed.2009.06.006
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发表时间：
2009-10-01
影响因子：
2.6
通讯作者：
Branscum, Adam J.
中科院分区：
文献类型：
--
作者：
Johnson, Wesley O.;Gardner, Ian A.;Branscum, Adam J.
诊断测试准确性的贝叶斯分析通常需要假设群体之间的测试准确性恒定，以确保模型的可识别性。在之前的一项研究中（Toft, N., Jorgensen, E., Hojsgaard, S., 2005。诊断诊断测试：评估在没有金标准的情况下估计敏感性和特异性的假设。Prev. Vet. Med. 68,19-33），来自两个测试的两个群体模型的敏感性估计被证明对感染率较高的群体进行了加权。在本研究中，我们提供了分析公式，可以深入了解当假设不正确时假设恒定灵敏度的效果。为了进一步研究恒定敏感性假设失败的影响，我们还假设第一个测试的敏感性在两个群体中存在差异，模拟了几个数据集。在 WinBUGS 中实现了假定灵敏度恒定的贝叶斯条件独立模型，并根据已知的参数真实值评估后验估计（平均值和 95% 概率区间）。对几种情况的贝叶斯分析结果表明，当一项测试完全具体时，后验平均值是对两个群体敏感性的加权平均值的良好估计。当两个测试都不是完全特异性时，测试 1 敏感性的贝叶斯后验平均值要么大于两个真实敏感性中的较大者，要么小于两者，并且对患病率和第二个测试的特异性的估计是不正确的。这意味着，如果测试敏感性在人群中不是恒定的，则某些参数的估计将会出现偏差。如果没有完全具体的测试，并且如果恒定灵敏度的假设失败，我们知道的唯一解决方案将涉及合并至少两个参数的先验信息。 (C) 2009 Elsevier B.V. 保留所有权利。
Bayesian analyses of diagnostic test accuracy often require the assumption of constant test accuracy among populations to ensure model identifiability. In a prior study (Toft, N., Jorgensen, E., Hojsgaard, S., 2005. Diagnosing diagnostic tests: evaluating the assumptions underlying the estimation of sensitivity and specificity in the absence of a gold standard. Prev. Vet. Med. 68,19-33), the sensitivity estimate from a two-test two-population model was shown to be weighted toward the population with the higher prevalence of infection. In the present study, we provided analytical formulae that give insight into the effect of assuming constant sensitivity when this assumption was false. To further investigate the effect of failure of the assumption of constant sensitivity, we also simulated several data sets under the assumption that the first test's sensitivity varied in the two populations. Bayesian conditional independence models that presumed constant sensitivities were implemented in WinBUGS and posterior estimates (mean and 95% probability intervals) were evaluated based on the known true values of the parameters. Findings from the Bayesian analyses of several scenarios indicated that the posterior mean was a good estimate of the weighted mean of the sensitivities in the two populations, when one test was perfectly specific. When neither test was perfectly specific, the Bayesian posterior mean for test 1 sensitivity was either greater than the larger of the two true sensitivities, or smaller than both, and estimates of prevalence and the second test's specificity were incorrect. The implication is that estimates of some parameters will be biased if test sensitivities are not constant across populations. Without a perfectly specific test, and if the assumption of constant sensitivity fails, the only solution we are aware of would involve incorporating prior information on at least two parameters. (C) 2009 Elsevier B.V. All rights reserved.