J Gerß1, M Eveslage, A Faldum, R Schmidt. 1. Institut für Biometrie und Klinische Forschung (IBKF), Westfälische Wilhelms-Universität Münster, Schmeddingstr. 56, 48149, Münster, Deutschland, joachim.gerss@ukmuenster.de.
Abstract
BACKGROUND: Progress in the field of medical research requires further development of clinical trial methodology to overcome the challenges resulting from small patient populations and restricted resources. METHODS: Classical single-stage designs with fixed sample sizes do not allow for interim analyses or design modifications. In contrast, adaptive designs adhere to established quality criteria while providing flexibility when conducting a clinical trial. In the face of new discoveries or information collected in the course of a trial, sample size adjustment, the selection of the target population and further design modifications can be performed. This enhances the chance of success of a clinical trial. Besides adaptive designs, classical approaches may be replaced or complemented by Bayesian methods. In a Bayesian approach prior knowledge can be efficiently included and hence the amount of information utilized in statistical analyses is increased. Furthermore, Bayes procedures allow the results of a statistical evaluation to be displayed very clearly. CONCLUSION: Modern approaches, such as adaptive designs and Bayesian designs overcome the challenges in clinical research due to enhanced flexibility and efficiency. In addition, both approaches can be combined.
BACKGROUND: Progress in the field of medical research requires further development of clinical trial methodology to overcome the challenges resulting from small patient populations and restricted resources. METHODS: Classical single-stage designs with fixed sample sizes do not allow for interim analyses or design modifications. In contrast, adaptive designs adhere to established quality criteria while providing flexibility when conducting a clinical trial. In the face of new discoveries or information collected in the course of a trial, sample size adjustment, the selection of the target population and further design modifications can be performed. This enhances the chance of success of a clinical trial. Besides adaptive designs, classical approaches may be replaced or complemented by Bayesian methods. In a Bayesian approach prior knowledge can be efficiently included and hence the amount of information utilized in statistical analyses is increased. Furthermore, Bayes procedures allow the results of a statistical evaluation to be displayed very clearly. CONCLUSION: Modern approaches, such as adaptive designs and Bayesian designs overcome the challenges in clinical research due to enhanced flexibility and efficiency. In addition, both approaches can be combined.
Authors: Werner Brannath; Emmanuel Zuber; Michael Branson; Frank Bretz; Paul Gallo; Martin Posch; Amy Racine-Poon Journal: Stat Med Date: 2009-05-01 Impact factor: 2.373