Bayesian clinical trial design using historical data that inform the treatment effect.

TitleBayesian clinical trial design using historical data that inform the treatment effect.
Publication TypeJournal Article
Year of Publication2019
AuthorsPsioda, Matthew A., and Joseph G. Ibrahim
Date Published2019 Jul 01
KeywordsBayes Theorem, Biostatistics, Clinical Trials as Topic, Computer Simulation, Humans, Melanoma, Models, Statistical, Research Design, Sample Size

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.

Alternate JournalBiostatistics
Original PublicationBayesian clinical trial design using historical data that inform the treatment effect.
PubMed ID29547966
PubMed Central IDPMC6587921
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States