A practical guide to Bayesian group sequential designs†
Corresponding Author
Thomas Gsponer
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
Correspondence to: Heinz Schmidli, Novartis Pharma AG, PO Box, CH-4002 Basel, Switzerland.
E-mail: [email protected]
Search for more papers by this authorFlorian Gerber
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
Search for more papers by this authorBjörn Bornkamp
Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
Search for more papers by this authorDavid Ohlssen
Statistical Methodology, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
Search for more papers by this authorMarc Vandemeulebroecke
Integrated Information Sciences, Novartis Pharma AG, Basel, Switzerland
Search for more papers by this authorHeinz Schmidli
Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
Search for more papers by this authorCorresponding Author
Thomas Gsponer
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
Correspondence to: Heinz Schmidli, Novartis Pharma AG, PO Box, CH-4002 Basel, Switzerland.
E-mail: [email protected]
Search for more papers by this authorFlorian Gerber
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
Search for more papers by this authorBjörn Bornkamp
Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
Search for more papers by this authorDavid Ohlssen
Statistical Methodology, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
Search for more papers by this authorMarc Vandemeulebroecke
Integrated Information Sciences, Novartis Pharma AG, Basel, Switzerland
Search for more papers by this authorHeinz Schmidli
Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
Search for more papers by this authorAbstract
Bayesian approaches to the monitoring of group sequential designs have two main advantages compared with classical group sequential designs: first, they facilitate implementation of interim success and futility criteria that are tailored to the subsequent decision making, and second, they allow inclusion of prior information on the treatment difference and on the control group. A general class of Bayesian group sequential designs is presented, where multiple criteria based on the posterior distribution can be defined to reflect clinically meaningful decision criteria on whether to stop or continue the trial at the interim analyses. To evaluate the frequentist operating characteristics of these designs, both simulation methods and numerical integration methods are proposed, as implemented in the corresponding R package gsbDesign. Normal approximations are used to allow fast calculation of these characteristics for various endpoints. The practical implementation of the approach is illustrated with several clinical trial examples from different phases of drug development, with various endpoints, and informative priors. Copyright © 2013 John Wiley & Sons, Ltd.
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Citing Literature
Special Issue:Bayesian Methods in Drug Development and Regulatory Review
January/Febuary 2014
Pages 71-80
