Simultaneous inference for partial areas under receiver operating curves -- with a view towards efficiency

受试者工作特征曲线的非参数协变量调整

Nonparametric Covariate Adjustment for Receiver Operating Characteristic Curves

诊断测试的准确性通常使用受试者工作特征（ROC）曲线来表征。ROC曲线下面积（AUC）等总结指标用于比较不同的测试以及测量两个人群之间的差异。通常可以获得关于已知会影响此类测量准确性的某些协变量的额外信息。我们提出了非参数方法的协变量调整的AUC。分别讨论和分析了具有正态误差和非正态误差的模型。非参数回归用于估计这两种情况下的均值和方差函数。在一般的噪声情况下，我们提出了一个协变量调整的曼-惠特尼估计AUC估计，有效地利用现有的数据来构建工作样本在任何协变量值的兴趣，是计算效率的实施。这提供了一个概括的曼-惠特尼方法比较两个群体考虑协变量的影响。我们推导了AUC估计在这两种情况下的渐近性质，包括渐近正态性、最优强一致收敛速度和均方误差一致性。所提出的方法的实用性证明通过模拟和真实数据的例子。

疾病状态存在不可忽略缺失时的受试者工作特征曲线分析

Receiver operating characteristic curve analysis with non-ignorable missing disease status

针对疾病状态存在不可忽略缺失的医疗数据，本文在逻辑回归框架下确立模型参数可识别性，提出似然估计方法获取模型参数、ROC曲线及AUC并推导其渐近分布，通过模拟验证方法性能并应用于实际数据集。

连续诊断测试受试者工作特征曲面估计的偏差校正方法

Bias-corrected methods for estimating the receiver operating characteristic surface of continuous diagnostic tests

针对三分类疾病状态下连续诊断测试评估中的验证偏差问题，本文提出了ROC曲面及其曲面下体积的验证偏差校正估计量，证明了其一致性与渐近正态性，并通过模拟研究和实例验证了方法的有效性。

Confidence intervals for the area under the receiver operating characteristic curve in the presence of ignorable missing data

Receiver operating characteristic (ROC) curves are widely used as a measure of accuracy of diagnostic tests and can be summarized using the area under the ROC curve (AUC). Often, it is useful to construct a confidence intervals for the AUC, however, since there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this manuscript, we compare different methods of constructing Wald-type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find …

Revisiting sample size planning for receiver operating characteristic studies: a confidence interval approach with precision and assurance

Objectives: Estimation of areas under receiver operating characteristic curves (AUCs) and their differences is a key task in diagnostic studies. We aimed to derive, evaluate, and implement simple sample size formulas for such studies with a focus on estimation rather than hypothesis testing. Materials and Methods: Sample size formulas were developed by explicitly incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. A new variance function was proposed for val…

Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC)

In many binary classification applications such as disease diagnosis and spam detection, practitioners often face great needs to control type I errors (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, $α$, on the type I error. Although the NP paradigm has a century-long history i…