MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
Department of Statistics and Nuffield College, University of Oxford, Oxford, UK.
Eur J Epidemiol. 2022 Apr;37(4):377-387. doi: 10.1007/s10654-021-00805-w. Epub 2021 Oct 15.
Most studies of continuous health-related outcomes examine differences in mean levels (location) of the outcome by exposure. However, identifying effects on the variability (scale) of an outcome, and combining tests of mean and variability (location-and-scale), could provide additional insights into biological mechanisms. A joint test could improve power for studies of high-dimensional phenotypes, such as epigenome-wide association studies of DNA methylation at CpG sites. One possible cause of heterogeneity of variance is a variable interacting with exposure in its effect on outcome, so a joint test of mean and variability could help in the identification of effect modifiers. Here, we review a scale test, based on the Brown-Forsythe test, for analysing variability of a continuous outcome with respect to both categorical and continuous exposures, and develop a novel joint location-and-scale score (JLSsc) test. These tests were compared to alternatives in simulations and used to test associations of mean and variability of DNA methylation with gender and gestational age using data from the Accessible Resource for Integrated Epigenomics Studies (ARIES). In simulations, the Brown-Forsythe and JLSsc tests retained correct type I error rates when the outcome was not normally distributed in contrast to the other approaches tested which all had inflated type I error rates. These tests also identified > 7500 CpG sites for which either mean or variability in cord blood methylation differed according to gender or gestational age. The Brown-Forsythe test and JLSsc are robust tests that can be used to detect associations not solely driven by a mean effect.
大多数关于连续健康相关结果的研究都是通过暴露来检验结果的平均水平（位置）差异。然而，确定对结果变异性（规模）的影响，并结合对均值和变异性（位置和规模）的检验，可以为生物机制提供更多的见解。联合检验可以提高高维表型（如 CpG 位点 DNA 甲基化的全基因组关联研究）研究的功效。方差异质性的一个可能原因是暴露对结果的影响与其相互作用的变量，因此，均值和变异性的联合检验可以帮助识别效应修饰因子。在这里，我们回顾了一种基于布朗-福塞思检验的规模检验，用于分析连续结果的变异性与分类和连续暴露的关系，并开发了一种新的联合位置和规模评分（JLSsc）检验。这些检验方法在模拟中与其他方法进行了比较，并用于分析 ARIES 数据中 DNA 甲基化的均值和变异性与性别和胎龄的相关性。在模拟中，与其他测试方法相比，布朗-福塞思检验和 JLSsc 检验在结果呈非正态分布时保留了正确的Ⅰ型错误率，而其他测试方法的Ⅰ型错误率均偏高。这些检验方法还确定了 7500 多个 CpG 位点，其脐带血甲基化的均值或变异性根据性别或胎龄而有所不同。布朗-福塞思检验和 JLSsc 是稳健的检验方法，可以用于检测不仅仅由均值效应驱动的相关性。
