Equivalence of regression curves sharing common parameters

  • In clinical trials, the comparison of two different populations is a common problem. Nonlinear (parametric) regression models are commonly used to describe the relationship between covariates, such as concentration or dose, and a response variable in the two groups. In some situations, it is reasonable to assume some model parameters to be the same, for instance, the placebo effect or the maximum treatment effect. In this paper, we develop a (parametric) bootstrap test to establish the similarity of two regression curves sharing some common parameters. We show by theoretical arguments and by means of a simulation study that the new test controls its significance level and achieves a reasonable power. Moreover, it is demonstrated that under the assumption of common parameters, a considerably more powerful test can be constructed compared with the test that does not use this assumption. Finally, we illustrate the potential applications of the new methodology by a clinical trial example.

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Metadaten
Author:Kathrin MöllenhoffORCiDGND, Frank BretzORCiDGND, Holger DetteORCiDGND
URN:urn:nbn:de:hbz:294-81762
DOI:https://doi.org/10.1111/biom.13149
Parent Title (English):Biometrics
Publisher:Wiley
Place of publication:Weinheim
Document Type:Article
Language:English
Date of Publication (online):2021/06/23
Date of first Publication:2019/09/13
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Tag:dose‐finding studies; equivalence testing; nonlinear regression; parametric bootstrap; similarity of regression curves
Volume:76
Issue:2
First Page:518
Last Page:529
Note:
Dieser Beitrag ist auf Grund des DEAL-Wiley-Vertrages frei zugänglich.
Institutes/Facilities:Lehrstuhl für Stochastik
Dewey Decimal Classification:Naturwissenschaften und Mathematik / Mathematik
open_access (DINI-Set):open_access
faculties:Fakultät für Mathematik
Licence (English):License LogoCreative Commons - CC BY 4.0 - Attribution 4.0 International