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A practical guide to Bayesian group sequential designs
Author(s) -
Gsponer Thomas,
Gerber Florian,
Bornkamp Björn,
Ohlssen David,
Vandemeulebroecke Marc,
Schmidli Heinz
Publication year - 2013
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1593
Subject(s) - frequentist inference , prior probability , bayesian probability , interim , computer science , interim analysis , posterior probability , sequential analysis , bayesian inference , machine learning , clinical trial , artificial intelligence , statistics , mathematics , medicine , archaeology , pathology , history
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.