Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model
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Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model. / Ozenne, Brice; Budtz-Jorgensen, Esben; Ebert, Sebastian Elgaard.
I: Computational Statistics, Bind 38, 2023, s. 1-23.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model
AU - Ozenne, Brice
AU - Budtz-Jorgensen, Esben
AU - Ebert, Sebastian Elgaard
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Latent variable model
KW - Multiple comparisons
KW - Max-test procedure
KW - Familywise error rate
KW - PARAMETERS
KW - MAXIMA
KW - TESTS
U2 - 10.1007/s00180-022-01214-7
DO - 10.1007/s00180-022-01214-7
M3 - Journal article
VL - 38
SP - 1
EP - 23
JO - Computational Statistics
JF - Computational Statistics
SN - 0943-4062
ER -
ID: 302379361