Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change
Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change
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DOI：
10.1016/s0306-4530(02)00108-7
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发表时间：
2003-10-01
影响因子：
3.7
通讯作者：
Hellhammer, DH
中科院分区：
文献类型：
--
作者：
Pruessner, JC;Kirschbaum, C;Hellhammer, DH
内分泌学研究和神经科学的研究方案经常采用随时间重复测量来记录生理或内分泌变量的变化。虽然人们希望获得重复的测量结果，以发现个体和群体在响应时间和持续时间方面的差异，但收集的数据量往往是统计分析的一个问题。当试图检测重复测量和其他变量之间可能的关联时，曲线下面积（AUC）通常用于合并多个时间点。然而，AUC的计算公式在实验室之间没有标准化，并且在讨论结果时通常不会呈现现有的差异，从而导致研究组之间的结果可能存在差异或不一致。本文从梯形公式出发，推导出两个计算曲线下面积的公式。这些公式被称为“相对于增加的曲线下面积”（AUC（L））和“相对于地面的曲线下面积”（AUC（G））。使用作者最近研究的人工和真实的数据，可以从这两个公式的重复测量中得到不同的信息。结果表明，根据使用的公式，不同的协会与其他变量可能会出现。因此，建议在分析重复测量的数据集时使用这两个公式。(C)2003爱思唯尔科技有限公司版权所有。
Study protocols in endocrinological research and the neurosciences often employ repeated measurements over time to record changes in physiological or endocrinological variables. While it is desirable to acquire repeated measurements for finding individual and group differences with regard to response time and duration, the amount of data gathered often represents a problem for the statistical analysis. When trying to detect possible associations between repeated measures and other variables, the area under the curve (AUC) is routinely used to incorporate multiple time points. However, formulas for computation of the AUC are not standardized across laboratories, and existing differences are usually not presented when discussing results, thus causing possible variability, or incompatibility of findings between research groups. In this paper, two formulas for calculation of the area under the curve are presented, which are derived from the trapezoid formula. These formulas are termed 'Area under the curve with respect to increase' (AUC(L)) and 'Area under the curve with respect to ground' (AUC(G)). The different information that can be derived from repeated measurements with these two formulas is exemplified using artificial and real data from recent studies of the authors. It is shown that depending on which formula is used, different associations with other variables may emerge. Consequently, it is recommended to employ both formulas when analyzing data sets with repeated measures. (C) 2003 Elsevier Science Ltd. All rights reserved.