Spritzler John, DeGruttola Victor G, Pei Lixia
Harvard University.
Int J Biostat. 2008 Jan 17;4(1):Article 1. doi: 10.2202/1557-4679.1068.
The commonly used two-sample tests of equal area-under-the-curve (AUC), where AUC is based on the linear trapezoidal rule, may have poor properties when observations are missing, even if they are missing completely at random (MCAR). We propose two tests: one that has good properties when data are MCAR and another that has good properties when the data are missing at random (MAR), provided that the pattern of missingness is monotonic. In addition, we discuss other non-parametric tests of hypotheses that are similar, but not identical, to the hypothesis of equal AUCs, but that often have better statistical properties than do AUC tests and may be more scientifically appropriate for many settings.
常用的基于线性梯形法则的曲线下面积(AUC)相等的两样本检验,在存在缺失观测值时可能性能不佳,即便这些观测值是完全随机缺失(MCAR)。我们提出了两种检验方法:一种在数据为MCAR时具有良好性能,另一种在数据为随机缺失(MAR)且缺失模式单调时具有良好性能。此外,我们还讨论了其他与AUC相等假设相似但不完全相同的非参数假设检验,这些检验通常比AUC检验具有更好的统计性能,并且在许多情况下可能在科学上更合适。