Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario, Canada.
Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
Stat Med. 2021 Jul 20;40(16):3808-3822. doi: 10.1002/sim.9000. Epub 2021 Apr 27.
Tests for variance or scale effects due to covariates are used in many areas and recently, in genomic and genetic association studies. We study score tests based on location-scale models with arbitrary error distributions that allow incorporation of additional adjustment covariates. Tests based on Gaussian and Laplacian double generalized linear models are examined in some detail. Numerical properties of the tests under Gaussian and other error distributions are examined. Our results show that the use of model-based asymptotic distributions with score tests for scale effects does not control type 1 error well in many settings of practical relevance. We consider simple statistics based on permutation distribution approximations, which correspond to well-known statistics derived by another approach. They are shown to give good type 1 error control under different error distributions and under covariate distribution imbalance. The methods are illustrated through a differential gene expression analysis involving breast cancer tumor samples.
检验由于协变量引起的方差或尺度效应在许多领域都有应用，最近在基因组学和遗传关联研究中也有应用。我们研究了基于位置-尺度模型的评分检验，该模型具有任意误差分布，可以纳入额外的调整协变量。我们详细研究了基于高斯和拉普拉斯双广义线性模型的检验。检验在高斯和其他误差分布下的数值性质也进行了研究。我们的结果表明，在许多实际相关的设置中，使用基于模型的渐近分布和评分检验进行尺度效应检验并不能很好地控制第一类错误。我们考虑了基于置换分布逼近的简单统计量，这些统计量对应于另一种方法得出的著名统计量。结果表明，它们在不同的误差分布和协变量分布不平衡的情况下，都能很好地控制第一类错误。这些方法通过涉及乳腺癌肿瘤样本的差异基因表达分析得到了说明。
