The Tukey trend test: Multiplicity adjustment using multiple marginal models
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The Tukey trend test: Multiplicity adjustment using multiple marginal models. / Schaarschmidt, Frank; Ritz, Christian; Hothorn, Ludwig A.
I: Biometrics, Bind 78, Nr. 2, 2022, s. 789-797.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - The Tukey trend test: Multiplicity adjustment using multiple marginal models
AU - Schaarschmidt, Frank
AU - Ritz, Christian
AU - Hothorn, Ludwig A
N1 - This article is protected by copyright. All rights reserved.
PY - 2022
Y1 - 2022
N2 - In dose-response analysis, it is a challenge to choose appropriate linear or curvilinear shapes when considering multiple, differently scaled endpoints. It has been proposed to fit several marginal regression models that try sets of different transformations of the dose levels as explanatory variables for each endpoint. However, the multiple testing problem underlying this approach, involving correlated parameter estimates for the dose effect between and within endpoints, could only be adjusted heuristically. An asymptotic correction for multiple testing can be derived from the score functions of the marginal regression models. Based on a multivariate t-distribution, the correction provides a one-step adjustment of p-values that accounts for the correlation between estimates from different marginal models. The advantages of the proposed methodology is demonstrated through three example data sets, involving generalized linear models with differently scaled endpoints, differing covariates and a mixed effect model and through simulation results. The methodology is implemented in an R package.
AB - In dose-response analysis, it is a challenge to choose appropriate linear or curvilinear shapes when considering multiple, differently scaled endpoints. It has been proposed to fit several marginal regression models that try sets of different transformations of the dose levels as explanatory variables for each endpoint. However, the multiple testing problem underlying this approach, involving correlated parameter estimates for the dose effect between and within endpoints, could only be adjusted heuristically. An asymptotic correction for multiple testing can be derived from the score functions of the marginal regression models. Based on a multivariate t-distribution, the correction provides a one-step adjustment of p-values that accounts for the correlation between estimates from different marginal models. The advantages of the proposed methodology is demonstrated through three example data sets, involving generalized linear models with differently scaled endpoints, differing covariates and a mixed effect model and through simulation results. The methodology is implemented in an R package.
KW - Faculty of Science
KW - Adjustment of p-values
KW - Dose-response
KW - Multiple endpoints
KW - Multivariate normal
KW - Toxicology
U2 - 10.1111/biom.13442
DO - 10.1111/biom.13442
M3 - Journal article
C2 - 33559878
VL - 78
SP - 789
EP - 797
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 2
ER -
ID: 256626097