The Tukey trend test: Multiplicity adjustment using multiple marginal models

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

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.

OriginalsprogEngelsk
TidsskriftBiometrics
ISSN0006-341X
DOI
StatusE-pub ahead of print - 9 feb. 2021

Bibliografisk note

CURIS 2021 NEXS 106

ID: 256626097