Diagnostic tests play an important role in medical research and clinical practice. The ultimate goal of a diagnostic test is to distinguish between diseased and nondiseased individuals and before a test is routinely used in practice, it is a pivotal requirement that its ability to discriminate between these two states is thoroughly assessed. The overlap coefficient, which is defined as the proportion of overlap area between two probability density functions, has gained popularity as a summary measure of diagnostic accuracy. We propose two Bayesian nonparametric estimators, based on Dirichlet process mixtures, for estimating the overlap coefficient. We further introduce the covariate-specific overlap coefficient and develop a Bayesian nonparametric approach based on Dirichlet process mixtures of additive normal models for estimating it. A simulation study is conducted to assess the empirical performance of our proposed estimators. Two illustrations are provided: one concerned with the search for biomarkers of ovarian cancer and another one aimed to assess the age-specific accuracy of glucose as a biomarker of diabetes.

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重叠系数的贝叶斯非参数推断：在疾病诊断中的应用

诊断测试在医学研究和临床实践中发挥着重要作用。诊断测试的最终目标是区分患病和未患病的个体，在实践中常规使用测试之前，对其区分这两种状态的能力进行彻底评估是一项关键要求。重叠系数，定义为两个概率密度函数之间重叠区域的比例，作为诊断准确性的总结性度量已经得到普及。我们提出了两个基于 Dirichlet 过程混合的贝叶斯非参数估计器来估计重叠系数。我们进一步介绍了协变量特定的重叠系数，并开发了一种基于加法正态模型的狄利克雷过程混合的贝叶斯非参数方法来估计它。进行模拟研究以评估我们提出的估计器的经验性能。提供了两幅插图：一幅与寻找卵巢癌的生物标志物有关，另一幅旨在评估葡萄糖作为糖尿病生物标志物的年龄特异性准确性。