Correlation-adjusted estimation of sensitivity and specificity of two diagnostic tests
Correlation-adjusted estimation of sensitivity and specificity of two diagnostic tests
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DOI：
10.1111/1467-9876.00389
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发表时间：
2003-01-01
影响因子：
1.6
通讯作者：
Singh, R
中科院分区：
文献类型：
--
作者：
Georgiadis, MP;Johnson, WO;Singh, R
多重测试筛选数据的模型通常需要假设这些测试是独立于疾病状态的。这种假设可能是不合理的，特别是当测试的生物学基础相同时。我们提出了一个模型，允许两个诊断测试结果之间的相关性。由于包含检验相关性的模型涉及的参数比可用数据估计的参数多，因此后验推断将更严重地依赖于先验分布，即使样本量很大。如果我们有关于两种筛选测试之一（可能是目前使用的标准测试）或测试人群的患病率的合理准确信息，则可以准确推断所有参数，包括测试相关性。我们提出了一个模型，用于评估相关的诊断测试和分析真实的和模拟数据集。我们的分析表明，当测试相关时，假设条件独立的模型可能表现得很差。我们建议，如果测试只是中等准确性和测量相同的生物反应，研究人员使用依赖模型进行分析。
Models for multiple-test screening data generally require the assumption that the tests are independent conditional on disease state. This assumption may be unreasonable, especially when the biological basis of the tests is the same. We propose a model that allows for correlation between two diagnostic test results. Since models that incorporate test correlation involve more parameters than can be estimated with the available data, posterior inferences will depend more heavily on prior distributions, even with large sample sizes. If we have reasonably accurate information about one of the two screening tests (perhaps the standard currently used test) or the prevalences of the populations tested, accurate inferences about all the parameters, including the test correlation, are possible. We present a model for evaluating dependent diagnostic tests and analyse real and simulated data sets. Our analysis shows that, when the tests are correlated, a model that assumes conditional independence can perform very poorly. We recommend that, if the tests are only moderately accurate and measure the same biological responses, researchers use the dependence model for their analyses.