Adaptive designs for confirmatory clinical trials |
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Authors: | Frank Bretz Franz Koenig Werner Brannath Ekkehard Glimm Martin Posch |
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Affiliation: | 1. Novartis Pharma AG, Lichtstrasse 35, 4002 Basel, Switzerland;2. Department of Biometry, Medical University of Hannover, 30623 Hannover, Germany;3. The first two authors (in alphabetic order) have made equal contributions to this paper.;4. Section of Medical Statistics, Medical University of Vienna, Spitalgasse 23, 1090 Wien, Austria |
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Abstract: | Adaptive designs play an increasingly important role in clinical drug development. Such designs use accumulating data of an ongoing trial to decide how to modify design aspects without undermining the validity and integrity of the trial. Adaptive designs thus allow for a number of possible adaptations at midterm: Early stopping either for futility or success, sample size reassessment, change of population, etc. A particularly appealing application is the use of adaptive designs in combined phase II/III studies with treatment selection at interim. The expectation has arisen that carefully planned and conducted studies based on adaptive designs increase the efficiency of the drug development process by making better use of the observed data, thus leading to a higher information value per patient. In this paper we focus on adaptive designs for confirmatory clinical trials. We review the adaptive design methodology for a single null hypothesis and how to perform adaptive designs with multiple hypotheses using closed test procedures. We report the results of an extensive simulation study to evaluate the operational characteristics of the various methods. A case study and related numerical examples are used to illustrate the key results. In addition we provide a detailed discussion of current methods to calculate point estimates and confidence intervals for relevant parameters. Copyright © 2009 John Wiley & Sons, Ltd. |
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Keywords: | adaptive seamless design design modification flexible design combination test conditional error rate interim analysis many‐to‐one comparisons treatment selection |
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