Evaluation of accuracy of diagnostic tests is frequently undertaken under nonignorable (NI) verification bias. Here, we discuss an approach, based on a mean score equation, aimed to estimate the volume under the receiver operating characteristic (ROC) surface of a diagnostic test under NI verification bias. The proposed approach rests on a parametric regression model for the verification process, which accommodates for possible NI missingness in the disease status of sample subjects, and may employ instrumental variables, to help avoid possible identifiability problems. The solution of the mean score equation derived from the verification model requires to preliminarily estimate the parameters of a model for the disease process, whose specification is limited to verified subjects. Verification bias‐corrected estimators, an alternative to those recently proposed in the literature and based on a full likelihood approach, are obtained from the estimated verification and disease probabilities. Consistency and asymptotic normality of the new estimators are established. Simulation experiments are conducted to evaluate their finite‐sample performances, and an application to a dataset from a research on epithelial ovarian cancer is presented. Although the narrative is driven by the three‐class case, the extension to high‐dimensional ROC analysis is also presented.

中文翻译：

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平均得分方程和工具变量：另一种方法是在数据丢失时随机估计接收器工作特性表面下的体积

诊断测试准确性的评估通常在不可忽略（NI）验证偏差下进行。在这里，我们讨论一种基于平均得分方程的方法，旨在估计在NI验证偏差下诊断测试的接收器工作特征（ROC）表面下的体积。所提出的方法基于用于验证过程的参数回归模型，该模型可以适应样本对象疾病状况中可能存在的NI缺失，并且可以使用工具变量来帮助避免可能的可识别性问题。从验证模型得出的平均得分方程的解需要预先估计疾病过程模型的参数，该模型的规范仅限于已验证的对象。验证偏差校正后的估算器，从估计的验证和疾病概率中获得了一种替代方法，这些替代方法是最近在文献中提出并基于完全似然法的那些方法。建立了新估计量的一致性和渐近正态性。进行了模拟实验以评估其有限样本性能，并提出了对上皮性卵巢癌研究的数据集应用。尽管叙述是由三类情况决定的，但也提出了对高维ROC分析的扩展。并将其应用于上皮性卵巢癌研究中的数据集。尽管叙述是由三类情况决定的，但也提出了对高维ROC分析的扩展。并将其应用于上皮性卵巢癌研究中的数据集。尽管叙述是由三类情况决定的，但也提出了对高维ROC分析的扩展。