Department of Biostatistics, The University of Texas at MD Anderson Cancer Center, Houston, Texas, USA.
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.
Stat Med. 2023 Mar 30;42(7):953-969. doi: 10.1002/sim.9652. Epub 2023 Jan 4.
Diagnostic tests usually need to operate at a high sensitivity or specificity level in practice. Accordingly, specificity at the controlled sensitivity, or vice versa, is a clinically sensible performance metric for evaluating continuous biomarkers. Meanwhile, the performance of a biomarker may vary across sub-populations as defined by covariates, and covariate-specific evaluation can be informative. In this article, we develop a novel modeling and estimation method for covariate-specific specificity at a controlled sensitivity level. Unlike existing methods which typically adopt elaborate models of covariate effects over the entire biomarker distribution, our approach models covariate effects locally at a specific sensitivity level of interest. We also extend our proposed model to handle the whole continuum of sensitivities via dynamic regression and derive covariate-specific ROC curves. We provide the variance estimation through bootstrapping. The asymptotic properties are established. We conduct extensive simulation studies to evaluate the performance of our proposed methods in comparison with existing methods, and further illustrate the applications in two clinical studies for aggressive prostate cancer.
诊断测试通常需要在实践中具有高灵敏度或特异性水平。因此，在控制灵敏度下的特异性，或者反之，是评估连续生物标志物的临床合理性能指标。同时，生物标志物的性能可能因协变量定义的亚人群而有所不同，并且协变量特异性评估可能具有信息性。在本文中，我们开发了一种新的建模和估计方法，用于在控制灵敏度水平下的协变量特异性。与通常采用整个生物标志物分布上的协变量效应精细模型的现有方法不同，我们的方法在感兴趣的特定灵敏度水平上局部建模协变量效应。我们还通过动态回归将我们提出的模型扩展到处理整个灵敏度范围，并得出协变量特异性 ROC 曲线。我们通过自举法提供方差估计。建立了渐近性质。我们进行了广泛的模拟研究，以比较我们提出的方法与现有方法的性能，并进一步在两个用于侵袭性前列腺癌的临床研究中说明了其应用。
