Soave David, Sun Lei
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5T 3M7, Canada.
Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada.
Biometrics. 2017 Sep;73(3):960-971. doi: 10.1111/biom.12651. Epub 2017 Jan 18.
We generalize Levene's test for variance (scale) heterogeneity between k groups for more complex data, when there are sample correlation and group membership uncertainty. Following a two-stage regression framework, we show that least absolute deviation regression must be used in the stage 1 analysis to ensure a correct asymptotic χk-12/(k-1) distribution of the generalized scale (gS) test statistic. We then show that the proposed gS test is independent of the generalized location test, under the joint null hypothesis of no mean and no variance heterogeneity. Consequently, we generalize the recently proposed joint location-scale (gJLS) test, valuable in settings where there is an interaction effect but one interacting variable is not available. We evaluate the proposed method via an extensive simulation study and two genetic association application studies.
当存在样本相关性和组成员身份不确定性时，我们将用于检验k组之间方差（尺度）异质性的Levene检验推广到更复杂的数据。遵循两阶段回归框架，我们表明在第一阶段分析中必须使用最小绝对偏差回归，以确保广义尺度（gS）检验统计量具有正确的渐近χk-12/(k-1)分布。然后我们表明，在均值和方差均无异质性的联合原假设下，所提出的gS检验独立于广义位置检验。因此，我们推广了最近提出的联合位置尺度（gJLS）检验，该检验在存在交互效应但一个交互变量不可用时很有价值。我们通过广泛的模拟研究和两项基因关联应用研究对所提出的方法进行了评估。
