Lu Di, Zhou Chunxiao, Tang Larry, Tan Ming, Yuan Ao, Chan Leighton
Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, 20057, Washington,DC, USA.
Rehabilitation Medicine Department, National Institutes of Health, Bethesda, 20892, MD, USA.
Stat Med. 2018 Apr 25. doi: 10.1002/sim.7688.
Evaluating the accuracy (ie, estimating the sensitivity and specificity) of new diagnostic tests without the presence of a gold standard is of practical meaning and has been the subject of intensive study for several decades. Existing methods use 2 or more diagnostic tests under several basic assumptions and then estimate the accuracy parameters via the maximum likelihood estimation. One of the basic assumptions is the conditional independence of the tests given the disease status. This assumption is impractical in many real applications in veterinary research. Several methods have been proposed with various dependence models to relax this assumption. However, these methods impose subjective dependence structures, which may not be practical and may introduce additional nuisance parameters. In this article, we propose a simple method for addressing this problem without the conditional independence assumption, using an empirical conditioning approach. The proposed method reduces to the popular Hui-Walter model in the case of conditional independence. Also, our likelihood function is of order-2 polynomial in parameters, while that of Hui-Walter is of order-3. The reduced model complexity increases the stability in estimation. Simulation studies are conducted to evaluate the performance of the proposed method, which shows overall smaller biases in estimation and is more stable than the existing method, especially when tests are conditionally dependent. Two real data examples are used to illustrate the proposed method.
在没有金标准的情况下评估新诊断测试的准确性（即估计敏感性和特异性）具有实际意义，并且几十年来一直是深入研究的主题。现有方法在几个基本假设下使用两种或更多种诊断测试，然后通过最大似然估计来估计准确性参数。其中一个基本假设是给定疾病状态下测试的条件独立性。这个假设在兽医研究的许多实际应用中是不切实际的。已经提出了几种方法，使用各种依赖模型来放宽这个假设。然而，这些方法强加了主观的依赖结构，这可能不实用并且可能引入额外的干扰参数。在本文中，我们提出了一种简单的方法来解决这个问题，而无需条件独立性假设，使用经验条件方法。在条件独立的情况下，所提出的方法简化为流行的Hui-Walter模型。此外，我们的似然函数在参数上是二阶多项式，而Hui-Walter的似然函数是三阶的。降低的模型复杂性增加了估计的稳定性。进行了模拟研究以评估所提出方法的性能，结果表明该方法总体上估计偏差较小，并且比现有方法更稳定，特别是当测试有条件依赖时。使用两个实际数据示例来说明所提出的方法。
