Assessing surveillance using sensitivity, specificity and timeliness
Assessing surveillance using sensitivity, specificity and timeliness
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DOI：
10.1177/0962280206071641
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发表时间：
2006-10-01
影响因子：
2.3
通讯作者：
Abrams, Allyson M.
中科院分区：
文献类型：
--
作者：
Kleinman, Ken P.;Abrams, Allyson M.
监测疾病的持续过程以发现突然的变化是实际流行病学和医学的一个重要方面。最常见的是，随着时间的推移，监控仅限于一维数据流。在这种情况下，工业过程监控的分析结果提出了监控数据流的最佳方法。包括空间位置和时间序列的数据流正在变得可用。结合空间数据的监测方法可能优于那些忽略空间数据的监测方法。然而，从分析角度来看，空间监测数据的最佳方法可能不存在。在本文中，我们介绍并讨论可用于比较监测统计方法性能的评估指标。我们的一般方法是概括受试者工作特征 (ROC) 曲线，除了通常的灵敏度和特异性测试特征之外，还纳入检测时间。除了通过两种及时性度量对普通 ROC 曲线进行加权之外，我们还描述了 ROC 曲线的三个三维概括，从而产生及时性 ROC 曲面。在监测疾病病例以检测突然爆发的背景下，我们在人工示例和先前描述的模拟环境中演示了这些内容，并展示了指标的不同之处。我们还讨论了差异以及在哪些情况下人们可能更喜欢某种给定的方法。
Monitoring ongoing processes of illness to detect sudden changes is an important aspect of practical epidemiology and medicine more generally. Most commonly, the monitoring has been restricted to a unidimensional stream of data over time. In such situations, analytic results from the industrial process monitoring have suggested optimal approaches to monitor the data streams. Data streams including spatial location as well as temporal sequence are becoming available. Monitoring methods that incorporate spatial data may prove superior to those that ignore it. However, analytically, optimal methods for spatial surveillance data may not exist. In the present article, we introduce and discuss evaluation metrics that can be used to compare the performance of statistical methods of surveillance. Our general approach is to generalize receiver operating characteristic (ROC) curves to incorporate the time of detection in addition to the usual test characteristics of sensitivity and specificity. In addition to weighting ordinary ROC curves by two measures of timeliness, we describe three three-dimensional generalizations of ROC curves that result in timeliness-ROC surfaces. Working in the context of surveillance of cases of disease to detect a sudden outbreak, we demonstrate these in an artificial example and in a previously described simulation context and show how the metrics differ. We also discuss the differences and under which circumstances one might prefer a given method.