Bayesian adaptive two-stage design for determining person-time in Phase II clinical trials with Poisson data |
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Authors: | Austin L. Hand John A. Scott James D. Stamey Dean M. Young |
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Affiliation: | 1. Quintiles, 4820 Emperor Blvd., Durham, NC, USA;2. Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Rockville, MD, USA;3. Department of Statistical Science, Baylor University, Waco, TX, USA |
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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 V. Sambucini, A Bayesian predictive two-stage design for Phase II clinical trials, Stat. Med. 27 (2008), pp. 1199–1224. doi: 10.1002/sim.3021[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]], 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). |
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Keywords: | Bayesian predictive distribution experimental efficacy conjugate prior count data Phase II clinical trials |
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