Pooling biospecimens and limits of detection: effects on ROC curve analysis
Pooling biospecimens and limits of detection: effects on ROC curve analysis
复制标题
DOI：
10.1093/biostatistics/kxj027
复制
发表时间：
2006-10-01
期刊：
影响因子：
2.1
通讯作者：
Liu, Aiyi
中科院分区：
文献类型：
--
作者：
Mumford, Sunni L.;Schisterman, Enrique F.;Liu, Aiyi
流行病学研究在评价生物标志物时经常遇到两个限制：成本和仪器灵敏度。成本可能会阻碍对新生物标志物有效性的评估。此外，许多测定受检测限（LOD）的影响，这取决于仪器的灵敏度。两种用于降低成本的常见策略包括从现有样本中随机抽取样本和合并生物样本。当LOD效应存在时，我们比较了两种采样策略。通过检查受试者工作特征（ROC）曲线分析的效率，特别是对正态分布标志物的ROC曲线下面积（AUC）的估计，对这些策略进行比较。我们提出并研究了一种方法来估计AUC处理数据时，从合并和未合并的样品中的LOD是有效的。总之，当LOD影响的数据少于50%时，池化是最有效的成本削减策略。但是，当超过50%的数据受到影响时，不建议使用池化设计。
Frequently, epidemiological studies deal with two restrictions in the evaluation of biomarkers: cost and instrument sensitivity. Costs can hamper the evaluation of the effectiveness of new biomarkers. In addition, many assays are affected by a limit of detection (LOD), depending on the instrument sensitivity. Two common strategies used to cut costs include taking a random sample of the available samples and pooling biospecimens. We compare the two sampling strategies when an LOD effect exists. These strategies are compared by examining the efficiency of receiver operating characteristic (ROC) curve analysis, specifically the estimation of the area under the ROC curve (AUC) for normally distributed markers. We propose and examine a method to estimate AUC when dealing with data from pooled and unpooled samples where an LOD is in effect. In conclusion, pooling is the most efficient cost-cutting strategy when the LOD affects less than 50% of the data. However, when much more than 50% of the data are affected, utilization of the pooling design is not recommended.