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Adaptive two-stage test procedures to find the best treatment in clinical trials

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Bischoff, Wolfgang ; Miller, Frank:
Adaptive two-stage test procedures to find the best treatment in clinical trials.
In: Biometrika. 92 (2005) 1. - S. 197-212.
ISSN 0006-3444 ; 1464-3510

Kurzfassung/Abstract

A main objective in clinical trials is to find the best treatment in a given finite class of competing treatments and then to show superiority of this treatment against a control treatment. The traditional procedure estimates the best treatment in a first trial. Then in an independent second trial superiority of this treatment, estimated as best in the first trial, is to be shown against the control treatment by a size $\alpha$ test.\par We investigate these two trials of this traditional procedure as a two-stage test procedure. Additionally we introduce competing two-stage group-sequential test procedures. Then we derive formulae for the expected number of patients. These formulae depend on unknown parameters. When we have a prior for the unknown parameters we can determine the two-stage test procedure of size $\alpha$ and power $\beta$ that is optimal, in that it needs a minimal number of observations. The results are illustrated by a numerical example, which indicates the superiority of the group-sequential procedures.

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Publikationsform:Artikel
Schlagwörter:Adaptive design; Bayes procedure; Clinical trial; Expected number of patients with respect to a prior; Group-sequential test; Two-stage test; Unknown variance
Institutionen der Universität:Mathematisch-Geographische Fakultät > Mathematik > Lehrstuhl für Mathematik - Statistik und Stochastik
Peer-Review-Journal:Ja
Verlag:Biometrika Trust
Die Zeitschrift ist nachgewiesen in:
Titel an der KU entstanden:Nein
KU.edoc-ID:2764
Eingestellt am: 23. Sep 2009 14:03
Letzte Änderung: 01. Jan 2010 21:33
URL zu dieser Anzeige: https://edoc.ku.de/id/eprint/2764/
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