Semi-parametric ROC regression analysis with placement values
Semi-parametric ROC regression analysis with placement values
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DOI：
10.1093/biostatistics/5.1.45
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发表时间：
2004-01-01
期刊：
影响因子：
2.1
通讯作者：
Cai, TX
中科院分区：
文献类型：
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作者：
Cai, TX
技术的进步为疾病的早期检测提供了新的诊断测试。通常，这些测试具有连续的结果。一个流行的方法来总结这种测试的准确性是受试者工作特征（ROC）曲线。估计ROC曲线的方法早已存在。为了检验协变量效应，Pepe（1997，2000）和Alonzo and Pepe（2002）提出了基于ROC曲线参数回归模型的无分布方法。Cai和Pepe（2002）通过允许任意非参数基线函数扩展了参数ROC回归模型。在本文中，虽然我们遵循相同的半参数设置，在该文件中，我们强调了一个新的估计，提供了几个改进的早期工作：上级效率，估计协变量的影响，而不估计非参数基线函数和易于实现的标准软件的能力。该方法应用于病例对照数据集，在该数据集中，我们评估了前列腺特异性抗原作为前列腺癌早期检测生物标志物的准确性。模拟研究表明，半参数模型下的新估计量虽然总是更稳健，但其效率与参数模型的Alonzo和Pepe（2002）估计量相当或更好。
Advances in technology provide new diagnostic tests for early detection of disease. Frequently, these tests have continuous outcomes. One popular method to summarize the accuracy of such a test is the Receiver Operating Characteristic (ROC) curve. Methods for estimating ROC curves have long been available. To examine covariate effects, Pepe (1997, 2000) and Alonzo and Pepe (2002) proposed distribution-free approaches based on a parametric regression model for the ROC curve. Cai and Pepe (2002) extended the parametric ROC regression model by allowing an arbitrary non-parametric baseline function. In this paper, while we follow the same semi-parametric setting as in that paper, we highlight a new estimator that offers several improvements over the earlier work: superior efficiency, the ability to estimate the covariate effects without estimating the non-parametric baseline function and easy implementation with standard software. The methodology is applied to a case control dataset where we evaluate the accuracy of the prostate-specific antigen as a biomarker for early detection of prostate cancer. Simulation studies suggest that the new estimator under the semi-parametric model, while always being more robust, has efficiency that is comparable to or better than the Alonzo and Pepe (2002) estimator from the parametric model.