Bayesian adaptive two-stage design for determining person-time in Phase II clinical trials with Poisson data

Austin L. Hand, John A. Scott, Phil D. Young, James Stamey and Dean M. Young

Journal of Applied Statistics, 2016, vol. 43, issue 9, 1625-1635

Abstract: Adaptive clinical trial designs can often improve drug-study efficiency by utilizing data obtained during the course of the trial. We present a novel Bayesian two-stage adaptive design for Phase II clinical trials with Poisson-distributed outcomes that allows for person-observation-time adjustments for early termination due to either futility or efficacy. Our design is motivated by the adaptive trial from [9], which uses binomial data. Although many frequentist and Bayesian two-stage adaptive designs for count data have been proposed in the literature, many designs do not allow for person-time adjustments after the first stage. This restriction limits flexibility in the study design. However, our proposed design allows for such flexibility by basing the second-stage person-time on the first-stage observed-count data. We demonstrate the implementation of our Bayesian predictive adaptive two-stage design using a hypothetical Phase II trial of Immune Globulin (Intravenous).

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:43:y:2016:i:9:p:1625-1635

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DOI: 10.1080/02664763.2015.1117588

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