Authors: Möllenhoff, Kathrin
Bretz, Frank
Dette, Holger
Title: Equivalence of regression curves sharing common parameters
Language (ISO): en
Abstract: In clinical trials the comparison of two different populations is a frequently addressed problem. Non-linear (parametric) regression models are commonly used to describe the relationship between covariates as the 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 level and achieves a reasonable power. Moreover, it is demonstrated that under the assumption of common parameters a considerable more powerful test can be constructed compared to the test which does not use this assumption. Finally, we illustrate potential applications of the new methodology by a clinical trial example.
Subject Headings: similarity of regression curves
dose finding studies
nonlinear regression
parametric bootstrap
equivalence testing
Subject Headings (RSWK): Nichtlineare Regression
Bootstrap-Statistik
Klinisches Experiment
URI: http://hdl.handle.net/2003/37915
http://dx.doi.org/10.17877/DE290R-19902
Issue Date: 2019
Appears in Collections:Sonderforschungsbereich (SFB) 823

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