Title | Bayesian clinical trial design using historical data that inform the treatment effect. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Psioda, Matthew A., and Joseph G. Ibrahim |
Journal | Biostatistics |
Volume | 20 |
Issue | 3 |
Pagination | 400-415 |
Date Published | 2019 Jul 01 |
ISSN | 1468-4357 |
Keywords | Bayes Theorem, Biostatistics, Clinical Trials as Topic, Computer Simulation, Humans, Melanoma, Models, Statistical, Research Design, Sample Size |
Abstract | We consider the problem of Bayesian sample size determination for a clinical trial in the presence of historical data that inform the treatment effect. Our broadly applicable, simulation-based methodology provides a framework for calibrating the informativeness of a prior while simultaneously identifying the minimum sample size required for a new trial such that the overall design has appropriate power to detect a non-null treatment effect and reasonable type I error control. We develop a comprehensive strategy for eliciting null and alternative sampling prior distributions which are used to define Bayesian generalizations of the traditional notions of type I error control and power. Bayesian type I error control requires that a weighted-average type I error rate not exceed a prespecified threshold. We develop a procedure for generating an appropriately sized Bayesian hypothesis test using a simple partial-borrowing power prior which summarizes the fraction of information borrowed from the historical trial. We present results from simulation studies that demonstrate that a hypothesis test procedure based on this simple power prior is as efficient as those based on more complicated meta-analytic priors, such as normalized power priors or robust mixture priors, when all are held to precise type I error control requirements. We demonstrate our methodology using a real data set to design a follow-up clinical trial with time-to-event endpoint for an investigational treatment in high-risk melanoma. |
DOI | 10.1093/biostatistics/kxy009 |
Alternate Journal | Biostatistics |
Original Publication | Bayesian clinical trial design using historical data that inform the treatment effect. |
PubMed ID | 29547966 |
PubMed Central ID | PMC6587921 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 GM070335 / GM / NIGMS NIH HHS / United States |
Bayesian clinical trial design using historical data that inform the treatment effect.
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