Using ROC curves to choose minimally important change thresholds when sensitivity and specificity are valued equally: the forgotten lesson of pythagoras. theoretical considerations and an example application of change in health status.
Using ROC curves to choose minimally important change thresholds when sensitivity and specificity are valued equally: the forgotten lesson of pythagoras. theoretical considerations and an example application of change in health status.
复制标题
DOI：
10.1371/journal.pone.0114468
复制
发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Abel G
中科院分区：
文献类型：
--
作者：
Froud R;Abel G
受试者操作特征（ROC）曲线被用于确定衡量健康状况变化的尺度上的最小重要变化（MIC）阈值。在准连续的患者报告的结果测量中，例如测量具有可变临床轨迹的慢性疾病的变化的那些，敏感性和特异性通常同等地被重视。尽管方法学家同意这些应该被同等重视，但已经采取了不同的方法来使用ROC曲线估计MIC阈值。我们的目的是比较使用的不同方法与新方法，探索方法选择不同阈值的程度，并考虑差异对应答者分析结论的影响。使用图形方法，假设数据，和数据从一个大型随机对照试验的手动治疗腰痛，我们比较了两种现有的方法与一种新的方法，是基于1-灵敏度和1-特异性的平方和的总和。不同的估计器选择的阈值可能存在分歧。不同的估计器选择的截点取决于ROC空间中的截点与估计器描述的不同轮廓之间的关系。特别是，不对称性和可能的切割点的数量影响阈值的选择。MIC估计量的选择很重要。选择临界点的不同方法可导致实质性不同的MIC阈值，从而影响应答者分析结果和试验结论。当敏感性和特异性的值相等时，基于1-敏感性和1-特异性的最小平方和的估计量是优选的。与目前使用的其他方法不同，平方和方法选择的切割点总是有效地选择最接近ROC空间左上角的切割点，而不管ROC曲线的形状如何。
Receiver Operator Characteristic (ROC) curves are being used to identify Minimally Important Change (MIC) thresholds on scales that measure a change in health status. In quasi-continuous patient reported outcome measures, such as those that measure changes in chronic diseases with variable clinical trajectories, sensitivity and specificity are often valued equally. Notwithstanding methodologists agreeing that these should be valued equally, different approaches have been taken to estimating MIC thresholds using ROC curves. We aimed to compare the different approaches used with a new approach, exploring the extent to which the methods choose different thresholds, and considering the effect of differences on conclusions in responder analyses. Using graphical methods, hypothetical data, and data from a large randomised controlled trial of manual therapy for low back pain, we compared two existing approaches with a new approach that is based on the addition of the sums of squares of 1-sensitivity and 1-specificity. There can be divergence in the thresholds chosen by different estimators. The cut-point selected by different estimators is dependent on the relationship between the cut-points in ROC space and the different contours described by the estimators. In particular, asymmetry and the number of possible cut-points affects threshold selection. Choice of MIC estimator is important. Different methods for choosing cut-points can lead to materially different MIC thresholds and thus affect results of responder analyses and trial conclusions. An estimator based on the smallest sum of squares of 1-sensitivity and 1-specificity is preferable when sensitivity and specificity are valued equally. Unlike other methods currently in use, the cut-point chosen by the sum of squares method always and efficiently chooses the cut-point closest to the top-left corner of ROC space, regardless of the shape of the ROC curve.
登录
查看更多内容
影响因子：
4
作者：
Froud, Robert;Eldridge, Sandra;Underwood, Martin
通讯作者：
Underwood, Martin
DOI：
10.1016/j.berh.2005.03.003
发表时间：
2005-08-01
影响因子：
5.2
作者：
Ostelo, RWJG;de Vet, HCW
通讯作者：
de Vet, HCW
影响因子：
7.2
作者：
de Vet, Henrica C. W.;Terluin, Berend;Terwee, Caroline B.
通讯作者：
Terwee, Caroline B.
DOI：
10.1016/0021-9681(86)90038-x
发表时间：
1986-01-01
期刊：
JOURNAL OF CHRONIC DISEASES
影响因子：
--
作者：
DEYO, RA;CENTOR, RM
通讯作者：
CENTOR, RM
影响因子：
3.5
作者：
Terwee, CB;Dekker, FW;Bossuyt, PMM
通讯作者：
Bossuyt, PMM