|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|
|Date Published||2019 Jul 01|
|Keywords||Bayes 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.
|Original Publication||Bayesian clinical trial design using historical data that inform the treatment effect.|
|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.