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Assessing the similarity of dose response and target doses in two non‐overlapping subgroups
Author(s) -
Bretz Frank,
Möllenhoff Kathrin,
Dette Holger,
Liu Wei,
Trampisch Matthias
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7546
Subject(s) - covariate , confidence interval , similarity (geometry) , mathematics , equivalence (formal languages) , statistics , coverage probability , margin (machine learning) , interval (graph theory) , computer science , artificial intelligence , combinatorics , machine learning , discrete mathematics , image (mathematics)
We consider 2 problems of increasing importance in clinical dose finding studies. First, we assess the similarity of 2 non‐linear regression models for 2 non‐overlapping subgroups of patients over a restricted covariate space. To this end, we derive a confidence interval for the maximum difference between the 2 given models. If this confidence interval excludes the pre‐specified equivalence margin, similarity of dose response can be claimed. Second, we address the problem of demonstrating the similarity of 2 target doses for 2 non‐overlapping subgroups, using again an approach based on a confidence interval. We illustrate the proposed methods with a real case study and investigate their operating characteristics (coverage probabilities, Type I error rates, power) via simulation.