Department of Epidemiology and Biostatistics, 1020 Pine Avenue West, McGill University, Montreal, Que., Canada H3A 1A2.
Stat Med. 2010 Nov 20;29(26):2688-97. doi: 10.1002/sim.4037.
Diagnostic tests rarely provide perfect results. The misclassification induced by imperfect sensitivities and specificities of diagnostic tests must be accounted for when planning prevalence studies or investigations into properties of new tests. The previous work has shown that applying a single imperfect test to estimate prevalence can often result in very large sample size requirements, and that sometimes even an infinite sample size is insufficient for precise estimation because the problem is non-identifiable. Adding a second test can sometimes reduce the sample size substantially, but infinite sample sizes can still occur as the problem remains non-identifiable. We investigate the further improvement possible when three diagnostic tests are to be applied. We first develop methods required for studies when three conditionally independent tests are available, using different Bayesian criteria. We then apply these criteria to prototypic scenarios, showing that large sample size reductions can occur compared to when only one or two tests are used. As the problem is now identifiable, infinite sample sizes cannot occur except in pathological situations. Finally, we relax the conditional independence assumption, demonstrating in this once again non-identifiable situation that sample sizes may substantially grow and possibly be infinite. We apply our methods to the planning of two infectious disease studies, the first designed to estimate the prevalence of Strongyloides infection, and the second relating to estimating the sensitivity of a new test for tuberculosis transmission. The much smaller sample sizes that are typically required when three as compared to one or two tests are used should encourage researchers to plan their studies using more than two diagnostic tests whenever possible. User-friendly software is available for both design and analysis stages greatly facilitating the use of these methods.
诊断测试很少能提供完美的结果。在规划患病率研究或新测试特性研究时，必须考虑到诊断测试灵敏度和特异性不完善引起的误诊。以前的工作表明，应用单一的不完美测试来估计患病率通常会导致非常大的样本量要求，有时甚至无限的样本量不足以进行精确估计，因为问题是不可识别的。添加第二个测试有时可以大大减少样本量，但由于问题仍然不可识别，无限的样本量仍然可能发生。我们研究了当应用三个诊断测试时可能进一步改进的方法。我们首先使用不同的贝叶斯标准开发了在有三个条件独立测试的情况下进行研究所需的方法。然后，我们将这些标准应用于原型场景，结果表明与使用一个或两个测试相比，可以大大减少样本量。由于问题现在是可识别的，除非在病理情况下，否则不会出现无限的样本量。最后，我们放宽了条件独立性假设，再次证明在这种不可识别的情况下，样本量可能会大幅增加，并且可能是无限的。我们将我们的方法应用于两个传染病研究的规划，第一个旨在估计 Strongyloides 感染的患病率，第二个与估计新测试检测结核病传播的敏感性有关。与使用一个或两个测试相比，当使用三个测试时通常需要更小的样本量，这应该鼓励研究人员尽可能使用三个以上的诊断测试来规划他们的研究。有方便用户的软件可用于设计和分析阶段，极大地促进了这些方法的使用。
