Robust estimation of area under ROC curve using auxiliary variables in the presence of missing biomarker values.
Robust estimation of area under ROC curve using auxiliary variables in the presence of missing biomarker values.
复制标题
DOI：
10.1111/j.1541-0420.2010.01487.x
复制
发表时间：
2011-06
期刊：
影响因子：
1.9
通讯作者：
Johnson BA
中科院分区：
文献类型：
--
作者：
Long Q;Zhang X;Johnson BA
在医学研究中，受试者工作特征（ROC）曲线可用于评估生物标志物在诊断疾病或预测未来患病风险方面的表现。ROC曲线下面积（area under ROC curve， AUC）作为ROC曲线的总结性度量，在比较多条ROC曲线时得到了广泛的应用。在观察性研究中，由于存在缺失的生物标志物值，对AUC的估计往往会变得复杂，这意味着现有的AUC估计可能存在偏差。在本文中，我们开发了稳健的统计方法来估计ROC AUC，并且所提出的方法使用来自辅助变量的信息，这些辅助变量可能预测生物标志物的缺失或缺失的生物标志物值。我们对预测缺失的生物标志物值的辅助变量特别感兴趣。在随机缺失（MAR）的情况下，即生物标志物值的缺失仅取决于观察到的数据，如果正确指定，以辅助变量和疾病状态为条件，缺失概率模型或生物标志物值模型，我们的估计器具有一致性的吸引人的特征。在非随机缺失（MNAR）的情况下，即缺失可能取决于未观察到的生物标志物值，我们提出了一个敏感性分析来评估MNAR对ROC AUC估计的影响。研究了所提估计量的渐近性质，并在仿真研究中评估了它们的有限样本行为。这些方法进一步说明了使用数据，从孕妇抑郁症的研究。
In medical research, the receiver operating characteristic (ROC) curves can be used to evaluate the performance of biomarkers for diagnosing diseases or predicting the risk of developing a disease in the future. The area under the ROC curve (AUC), as a summary measure of ROC curves, is widely utilized, especially when comparing multiple ROC curves. In observational studies, the estimation of the AUC is often complicated by the presence of missing biomarker values, which means that the existing estimators of the AUC are potentially biased. In this article, we develop robust statistical methods for estimating the ROC AUC and the proposed methods use information from auxiliary variables that are potentially predictive of the missingness of the biomarkers or the missing biomarker values. We are particularly interested in auxiliary variables that are predictive of the missing biomarker values. In the case of missing at random (MAR), i.e., missingness of biomarker values only depends on the observed data, our estimators have the attractive feature of being consistent if one correctly specifies, conditional on auxiliary variables and disease status, either the model for the probabilities of being missing or the model for the biomarker values. In the case of missing not at random (MNAR), i.e., missingness may depend on the unobserved biomarker values, we propose a sensitivity analysis to assess the impact of MNAR on the estimation of the ROC AUC. The asymptotic properties of the proposed estimators are studied and their finite sample behaviors are evaluated in simulation studies. The methods are further illustrated using data from a study of maternal depression during pregnancy.
登录
查看更多内容
影响因子：
3.7
作者：
Scharfstein, DO;Rotnitzky, A;Robins, JM
通讯作者：
Robins, JM
影响因子：
0.8
作者：
ZHOU, XH
通讯作者：
ZHOU, XH
影响因子：
9.8
作者：
Fergerson, SS;Jamieson, DJ;Lindsay, M
通讯作者：
Lindsay, M
影响因子：
1.9
作者：
Kosinski, AS;Barnhart, HX
通讯作者：
Barnhart, HX
DOI：
10.1198/016214505000001339
发表时间：
2006-09-01
影响因子：
3.7
作者：
Rotnitzky, Andrea;Faraggi, David;Schisterman, Enrique
通讯作者：
Schisterman, Enrique