School of Mathematics, University of Edinburgh, Edinburgh, UK.
Department of Mathematics and CMA, NOVA School of Sciences and Technology, NOVA University of Lisbon, Caparica, Portugal.
Stat Med. 2021 Nov 20;40(26):5779-5795. doi: 10.1002/sim.9153. Epub 2021 Sep 1.
Diagnostic tests are of critical importance in health care and medical research. Motivated by the impact that atypical and outlying test outcomes might have on the assessment of the discriminatory ability of a diagnostic test, we develop a robust and flexible model for conducting inference about the covariate-specific receiver operating characteristic (ROC) curve that safeguards against outlying test results while also accommodating for possible nonlinear effects of the covariates. Specifically, we postulate a location-scale regression model for the test outcomes in both the diseased and nondiseased populations, combining additive regression B-splines and M-estimation for the regression function, while the distribution of the error term is estimated via a weighted empirical distribution function of the standardized residuals. The results of the simulation study show that our approach successfully recovers the true covariate-specific area under the ROC curve on a variety of conceivable test outcomes contamination scenarios. Our method is applied to a dataset derived from a prostate cancer study where we seek to assess the ability of the Prostate Health Index to discriminate between men with and without Gleason 7 or above prostate cancer, and if and how such discriminatory capacity changes with age.
诊断测试在医疗保健和医学研究中至关重要。鉴于非典型和异常测试结果可能对诊断测试的判别能力评估产生的影响，我们开发了一种稳健且灵活的模型，用于对协变量特异性接收者操作特征（ROC）曲线进行推断，该模型可防范异常测试结果，同时还可适应协变量的可能非线性影响。具体来说，我们假设在患病和非患病人群中，对测试结果进行位置-尺度回归模型，将加性回归 B 样条和 M 估计相结合用于回归函数，而误差项的分布则通过标准化残差的加权经验分布函数进行估计。模拟研究的结果表明，我们的方法在各种可想象的测试结果污染场景下成功地恢复了真实的协变量特异性 ROC 曲线下面积。我们的方法应用于源自前列腺癌研究的数据集，我们试图评估前列腺健康指数在区分有无 Gleason 7 或以上前列腺癌的男性方面的能力，以及这种判别能力是否以及如何随年龄变化。
