Kleinman Ken P, Abrams Allyson M
Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, 133 Brookline Avenue, Boston, MA 02215, USA.
Stat Methods Med Res. 2006 Oct;15(5):445-64. doi: 10.1177/0962280206071641.
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.
监测疾病的进展过程以发现突然变化是实用流行病学乃至医学的一个重要方面。通常，监测仅限于随时间变化的一维数据流。在这种情况下，工业过程监测的分析结果已提出监测数据流的最佳方法。包含空间位置以及时间序列的数据流正变得可用。纳入空间数据的监测方法可能比忽略空间数据的方法更具优势。然而，在分析上，可能不存在用于空间监测数据的最佳方法。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。我们的一般方法是推广接受者操作特征（ROC）曲线，除了常规的灵敏度和特异性测试特征外，还纳入检测时间。除了通过两种及时性度量对普通ROC曲线进行加权外，我们还描述了ROC曲线的三种三维推广形式，它们会产生及时性-ROC曲面。在监测疾病病例以发现突然爆发的背景下，我们在一个人工示例和先前描述的模拟环境中展示了这些指标，并说明这些指标是如何不同的。我们还讨论了差异以及在何种情况下可能更倾向于使用特定方法。
