Biostatics and Biomathematics Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B500, Seattle, WA 98109, USA.
Acad Radiol. 2013 Jul;20(7):863-73. doi: 10.1016/j.acra.2013.03.004. Epub 2013 Apr 17.
Studies evaluating a new diagnostic imaging test may select control subjects without disease who are similar to case subjects with disease in regard to factors potentially related to the imaging result. Selecting one or more controls that are matched to each case on factors such as age, comorbidities, or study site improves study validity by eliminating potential biases due to differential characteristics of readings for cases versus controls. However, it is not widely appreciated that valid analysis requires that the receiver operating characteristic (ROC) curve be adjusted for covariates. We propose a new computationally simple method for estimating the covariate-adjusted ROC curve that is appropriate in matched case-control studies.
We provide theoretical arguments for the validity of the estimator and demonstrate its application to data. We compare the statistical properties of the estimator with those of a previously proposed estimator of the covariate-adjusted ROC curve. We demonstrate an application of the estimator to data derived from a study of emergency medical services encounters where the goal is to diagnose critical illness in nontrauma, non-cardiac arrest patients. A novel bootstrap method is proposed for calculating confidence intervals.
The new estimator is computationally very simple, yet we show it yields values that approximate the existing, more complicated estimator in simulated data sets. We found that the new estimator has excellent statistical properties, with bias and efficiency comparable with the existing method.
In matched case-control studies, the ROC curve should be adjusted for matching covariates and can be estimated with the new computationally simple approach.
评估新的诊断成像测试的研究可能会选择没有疾病的对照受试者，这些对照受试者在与成像结果相关的潜在因素方面与患有疾病的病例受试者相似。选择一个或多个与每个病例在年龄、合并症或研究地点等因素上相匹配的对照，可以通过消除因病例和对照的读数特征不同而导致的潜在偏差，提高研究的有效性。然而，人们普遍没有意识到，有效的分析需要对协变量进行调整，以调整接收者操作特征（ROC）曲线。我们提出了一种新的、计算简单的方法，用于估计匹配病例对照研究中调整协变量的 ROC 曲线。
我们提供了对该估计器有效性的理论论证，并演示了其在数据中的应用。我们比较了该估计器的统计性质与以前提出的调整协变量的 ROC 曲线的估计器的统计性质。我们展示了该估计器在一项紧急医疗服务遭遇研究数据中的应用，该研究的目的是诊断非创伤性、非心搏骤停患者的危重病。提出了一种新的bootstrap方法来计算置信区间。
新的估计器计算非常简单，但我们表明，它在模拟数据集上产生的值与现有更复杂的估计器近似。我们发现，新的估计器具有极好的统计性质，其偏差和效率与现有方法相当。
在匹配病例对照研究中，ROC 曲线应根据匹配协变量进行调整，可以使用新的计算简单的方法进行估计。
