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

Biostatistics. 2019 Jul 1;20(3):400-415. doi: 10.1093/biostatistics/kxy009.


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.

Keywords: Bayesian power; Clinical trial design; Power prior; Sample size determination; Sampling prior; Type I error rate.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Bayes Theorem
  • Biostatistics / methods*
  • Clinical Trials as Topic*
  • Computer Simulation
  • Humans
  • Melanoma / drug therapy
  • Models, Statistical*
  • Research Design*
  • Sample Size