Bayesian sample size determination for prevalence and diagnostic test studies in the absence of a gold standard test
Bayesian sample size determination for prevalence and diagnostic test studies in the absence of a gold standard test
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DOI：
10.1111/j.0006-341x.2004.00183.x
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发表时间：
2004-06-01
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;Rahme, E;Joseph, L
规划研究涉及诊断测试是复杂的事实，几乎没有测试提供完全准确的结果。无论研究的主要目的是估计人群中疾病的患病率还是调查新诊断试验的性质，都必须考虑到诊断试验的不完善敏感性和特异性引起的错误分类。以前的工作样本量的要求，估计疾病的流行情况下，一个单一的不完美的测试显示非常大的差异，大小相比，假设一个完美的测试方法。在这篇文章中，我们扩展这些方法，包括两个条件独立的不完美的测试，并应用几个不同的标准贝叶斯样本量的确定设计这样的研究。我们既考虑了疾病流行率研究，也考虑了旨在评估诊断试验灵敏度和特异性的研究。由于问题是典型的不可识别的，我们调查的样本容量接近无穷大的参数估计的准确性的限制。通过传染病的两个例子，我们说明了两个测试时出现的样本量的变化。在一项研究中，而不是一个单一的测试。虽然在两个测试的情况下经常发现较小的样本量，但它们仍然可能过大，除非有关于所使用测试的灵敏度和特异性的准确信息。
Planning studies involving diagnostic tests is complicated by the fact that virtually no test provides perfectly accurate results. The misclassification induced by imperfect sensitivities and specificities of diagnostic tests must be taken into account, whether the primary goal of the study is to estimate the prevalence of a disease in a population or to investigate the properties of a new diagnostic test. Previous work on sample size requirements for estimating the prevalence of disease in the case of a single imperfect test showed very large discrepancies in size when compared to methods that assume a perfect test. In this article we extend these methods to include two conditionally independent imperfect tests, and apply several different criteria for Bayesian sample size determination to the design of such studies. We consider both disease prevalence studies and studies designed to estimate the sensitivity and specificity of diagnostic tests. As the problem is typically non identifiable, we investigate the limits on the accuracy of parameter estimation as the sample size approaches infinity. Through two examples from infectious diseases, we illustrate the changes in sample sizes that arise when two tests are applied. to individuals in a study rather than a single test. Although smaller sample sizes are often found in the two-test situation, they can still be prohibitively large unless accurate information is available about the sensitivities and specificities of the tests being used.