Literature DB >> 17590875

Bayesian predictive power for interim adaptation in seamless phase II/III trials where the endpoint is survival up to some specified timepoint.

Heinz Schmidli1, Frank Bretz, Amy Racine-Poon.   

Abstract

Integration of a phase II and a phase III clinical trial into a single confirmatory study aims to shorten the development time without compromising the chance of success for a development program. These seamless phase II/III trials involve complex adaptations at the interim analysis, such as treatment selection, sample size reassessment, and stopping for futility. Bayesian methods can support these interim adaptations, and make this decision process more transparent. Use of a frequentist combination test for the final evaluation ensures that the type I error is controlled regardless of the adaptation rule employed at the interim analysis. In this paper, an adaptive seamless phase II/III trial design is proposed for studies where the endpoint is survival up to some specified timepoint and where Bayesian predictive power (PP) guides interim adaptations. For the evaluation of PP at the interim analysis, the event time is modelled as a piecewise exponential distribution, with informative priors for the hazard rates. As an illustrative example, regimen selection at interim in a four-arm trial with an active control is considered, where both non-inferiority and superiority to the control arm are tested. Frequentist properties of the adaptation criterion based on Bayesian PP are assessed by simulations.

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Year:  2007        PMID: 17590875     DOI: 10.1002/sim.2957

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  7 in total

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2.  Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls.

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Review 3.  Progressive Staging of Pilot Studies to Improve Phase III Trials for Motor Interventions.

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5.  Sample Size Reassessment and Hypothesis Testing in Adaptive Survival Trials.

Authors:  Dominic Magirr; Thomas Jaki; Franz Koenig; Martin Posch
Journal:  PLoS One       Date:  2016-02-10       Impact factor: 3.240

6.  Use of Bayesian statistics in drug development: Advantages and challenges.

Authors:  Sandeep K Gupta
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7.  Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

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Journal:  Stat Biopharm Res       Date:  2020-07-29       Impact factor: 1.452

  7 in total

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