Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model

Abstract

In latent variable models (LVMs) it is possible to analyze multiple outcomes and to relate them to several explanatory variables. In this context many parameters are estimated and it is common to perform multiple tests, e.g. to investigate outcome-specific effects using Wald tests or to check the correct specification of the modeled mean and variance using a forward stepwise selection (FSS) procedure based on Score tests. Controlling the family-wise error rate (FWER) at its nominal level involves adjustment of the p-values for multiple testing. Because of the correlation between test statistics, the Bonferroni procedure is often too conservative. In this article, we extend the max-test procedure to the LVM framework for Wald and Score tests. Depending on the correlation between the test statistics, the max-test procedure is equivalent or more powerful than the Bonferroni procedure while also providing, asymptotically, a strong control of the FWER for non-iterative procedures. Using simulation studies, we assess the finite sample behavior of the max-test procedure for Wald and Score tests in LVMs. We apply our procedure to quantify the neuroinflammatory response to mild traumatic brain injury in nine brain regions.