Nonparametric and Semiparametric Group Sequential Methods for Comparing Accuracy of Diagnostic Tests
Nonparametric and Semiparametric Group Sequential Methods for Comparing Accuracy of Diagnostic Tests
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DOI：
10.1111/j.1541-0420.2008.01000.x
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发表时间：
2008-12-01
期刊：
影响因子：
1.9
通讯作者：
Zhou, Xiao-Hua
中科院分区：
文献类型：
--
作者：
Tang, Liansheng;Emerson, Scott S.;Zhou, Xiao-Hua
使用两种诊断试验的受试者工作特征（ROC）曲线比较两种诊断试验的准确性通常使用固定样本设计进行。另一方面，比较诊断方式所固有的人体实验要求对积累的数据进行定期监测，以解决与医学研究的伦理和效率有关的许多问题。迄今为止，很少有研究使用顺序抽样计划进行比较ROC研究，即使这些研究可能使用昂贵且不安全的诊断程序。本文提出了一种非参数组序贯设计方案。非参数序贯法采用ROC曲线统计下的非参数加权面积族（Wieand et al., 1989, Biometrika 76, 585-592）和组序贯抽样计划。我们举例说明了在非小细胞肺癌诊断筛查的背景下，这种非参数方法用于顺序比较ROC曲线的实现。我们还描述了一种基于比例风险模型的半参数序列方法。在仿真研究中，我们比较了非参数方法与半参数和参数分析的统计性质。结果表明，非参数方法对模型错配具有良好的鲁棒性和有限样本性能。
Comparison of the accuracy of two diagnostic tests using the receiver operating characteristic (ROC) curves from two diagnostic tests has been typically conducted using fixed sample designs. On the other hand, the human experimentation inherent in a comparison of diagnostic modalities argues for periodic monitoring of the accruing data to address many issues related to the ethics and efficiency of the medical study. To date, very little research has been done on the use of sequential sampling plans for comparative ROC studies, even when these studies may use expensive and unsafe diagnostic procedures. In this article we propose a nonparametric group sequential design plan. The nonparametric sequential method adapts a nonparametric family of weighted area under the ROC curve statistics (Wieand et al., 1989, Biometrika 76, 585-592) and a group sequential sampling plan. We illustrate the implementation of this nonparametric approach for sequentially comparing ROC curves in the context of diagnostic screening for nonsmall-cell lung cancer. We also describe a semiparametric sequential method based on proportional hazard models. We compare the statistical properties of the nonparametric approach with alternative semiparametric and parametric analyses in simulation studies. The results show the nonparametric approach is robust to model misspecification and has excellent finite-sample performance.