Department of Biostatistics, School of Public Health, Peking University, Beijing, China.
School of Mathematical Sciences, Peking University, Beijing, China.
Stat Med. 2022 Jul 20;41(16):3022-3038. doi: 10.1002/sim.9400. Epub 2022 Apr 5.
In diagnostic radiology, the multireader multicase (MRMC) design and the free-response receiver operating characteristics (FROC) method are often used in combination. The cross-correlated data generated by the MRMC-FROC study leads to difficulties in the corresponding analysis, and the need to include covariates in the model further complicates the subsequent analysis. In this paper, we propose a regression approach based on three new measures with good interpretability. The correlation structure of the original test results is taken directly into account in the estimation procedure. The proposed method also allows the inclusion of continuous or discrete covariates. Consistent and asymptotically normal estimators are derived for the new measures. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that the regression approach performs well under a wide range of scenarios. We also apply the proposed regression approach to a diagnostic study of computer-aided diagnosis in lung cancer.
在诊断放射学中,多读者多病例(MRMC)设计和自由响应接收器操作特性(FROC)方法经常结合使用。MRMC-FROC 研究产生的交叉相关数据导致相应分析困难,并且需要在模型中包含协变量进一步使后续分析复杂化。在本文中,我们提出了一种基于三个具有良好可解释性的新指标的回归方法。在估计过程中直接考虑原始测试结果的相关结构。该方法还允许包含连续或离散协变量。为新指标推导出一致和渐近正态的估计量。进行了模拟研究以评估所提出方法的性能。模拟结果表明,该回归方法在广泛的场景下表现良好。我们还将所提出的回归方法应用于肺癌计算机辅助诊断的诊断研究中。