Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests
Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests
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DOI：
10.1111/j.0006-341x.2001.00158.x
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发表时间：
2001-03-01
期刊：
影响因子：
1.9
通讯作者：
Joseph, L
中科院分区：
文献类型：
--
作者：
Dendukuri, N;Joseph, L
许多对多种诊断测试结果的分析都假定，在受试者的真实疾病状况给定的条件下，这些测试在统计上是相互独立的。在实际中，这一假定可能不成立，尤其是在没有一种测试是完全准确的金标准的情况下。考虑测试之间条件相关性的模型的经典推断要求至少使用四种不同的测试结果，以便获得可识别的解，但获得这么多测试结果并不总是可行的。我们使用贝叶斯方法在考虑测试之间可能存在条件相关性的同时，对疾病患病率和测试特性进行推断，特别是当我们只有两种测试时。我们提出了固定效应模型和随机效应模型。由于测试少于四种时问题不可识别，即使样本量很大，后验分布也强烈依赖于关于测试特性和疾病患病率的先验信息。如果事先高精度地知道测试之间的相关程度，那么我们的方法会对测试之间的相关性进行调整。否则，我们的方法会提供调整后的推断，其中包含了问题中固有的所有不确定性，通常会导致更宽的区间估计。我们使用来自一项关于柬埔寨难民在加拿大的类圆线虫感染患病率研究的数据来说明我们的方法。
Many analyses of results from multiple diagnostic tests assume the tests are statistically independent conditional on the true disease status of the subject. This assumption may be violated in practice, especially in situations where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tests requires that results from at least four different tests be used in order to obtain an identifiable solution, but it is not always feasible to have results from this many tests. We use a Bayesian approach to draw inferences about the disease prevalence and test properties while adjusting for the possibility of conditional dependence between tests, particularly when we have only two tests. We propose both fixed and random effects models. Since with fewer than four tests the problem is nonidentifiable, the posterior distributions are strongly dependent on the prior information about the test properties and the disease prevalence, even with large sample sizes. If the degree of correlation between the tests is known a priori with high precision, then our methods adjust for the dependence between the tests. Otherwise, our methods provide adjusted inferences that incorporate all of the uncertainty inherent in the problem, typically resulting in wider interval estimates. We illustrate our methods using data from a study on the prevalence of Strongyloides infection among Cambodian refugees to Canada.