The Tukey trend test: Multiplicity adjustment using multiple marginal models

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OriginalspracheEnglisch
Seiten (von - bis)789-797
Seitenumfang9
FachzeitschriftBIOMETRICS
Jahrgang78
Ausgabenummer2
Frühes Online-Datum9 Feb. 2021
PublikationsstatusVeröffentlicht - 5 Juli 2022

Abstract

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 are demonstrated through three example datasets, 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.

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The Tukey trend test: Multiplicity adjustment using multiple marginal models. / Schaarschmidt, Frank; Ritz, Christian; Hothorn, Ludwig A.
in: BIOMETRICS, Jahrgang 78, Nr. 2, 05.07.2022, S. 789-797.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Schaarschmidt F, Ritz C, Hothorn LA. The Tukey trend test: Multiplicity adjustment using multiple marginal models. BIOMETRICS. 2022 Jul 5;78(2):789-797. Epub 2021 Feb 9. doi: 10.1111/biom.13442
Schaarschmidt, Frank ; Ritz, Christian ; Hothorn, Ludwig A. / The Tukey trend test : Multiplicity adjustment using multiple marginal models. in: BIOMETRICS. 2022 ; Jahrgang 78, Nr. 2. S. 789-797.
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