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Assessing the similarity of dose response and target doses in two non-overlapping subgroups.
Bretz, Frank; Möllenhoff, Kathrin; Dette, Holger; Liu, Wei; Trampisch, Matthias.
Afiliação
  • Bretz F; Novartis Pharma AG, Basel, CH-4002, Switzerland.
  • Möllenhoff K; Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
  • Dette H; Department of Mathematics, Ruhr-Universität Bochum, Germany.
  • Liu W; Department of Mathematics, Ruhr-Universität Bochum, Germany.
  • Trampisch M; S3RI and School of Mathematics, University of Southampton, Southampton, SO17 1TB, UK.
Stat Med ; 37(5): 722-738, 2018 02 28.
Article em En | MEDLINE | ID: mdl-29181854
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Intervalos de Confiança / Ensaios Clínicos Fase II como Assunto / Relação Dose-Resposta a Droga Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Intervalos de Confiança / Ensaios Clínicos Fase II como Assunto / Relação Dose-Resposta a Droga Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article