Dendukuri Nandini, Rahme Elham, Bélisle Patrick, Joseph Lawrence
Department of Epidemiology and Biostatistics, 1020 Pine Avenue West, McGill University, Montreal, Québec H3A 1A2, Canada.
Biometrics. 2004 Jun;60(2):388-97. doi: 10.1111/j.0006-341X.2004.00183.x.
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 nonidentifiable, 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.
涉及诊断测试的研究规划很复杂，因为几乎没有测试能提供完全准确的结果。无论研究的主要目标是估计人群中疾病的患病率还是研究新诊断测试的特性，都必须考虑到诊断测试灵敏度和特异度不完善所导致的错误分类。之前关于在单一不完善测试情况下估计疾病患病率所需样本量的研究表明，与假设测试完美的方法相比，样本量存在很大差异。在本文中，我们将这些方法扩展到包括两个条件独立的不完善测试，并将几种不同的贝叶斯样本量确定标准应用于此类研究的设计。我们既考虑疾病患病率研究，也考虑旨在估计诊断测试灵敏度和特异度的研究。由于该问题通常不可识别，我们研究了当样本量趋近于无穷大时参数估计准确性的限制。通过两个传染病方面的例子，我们说明了在研究中对个体应用两个测试而非单个测试时样本量所发生的变化。尽管在两个测试的情况下样本量通常较小，但除非能获得所使用测试的灵敏度和特异度的准确信息，否则样本量仍可能大到令人望而却步。
