Bayesian design and analysis of active control clinical trials.

Abstract

We consider the design and analysis of active control clinical trials, i.e., clinical trials comparing an experimental treatment E to a control treatment C considered to be effective. Direct comparison of E to placebo P, or no treatment, is sometimes ethically unacceptable. Much discussion of the design and analysis of such clinical trials has focused on whether the comparison of E to C should be based on a test of the null hypothesis of equivalence, on a test of a nonnull hypothesis that the difference is of some minimally medically important size delta, or on one or two-sided confidence intervals. These approaches are essentially the same for study planning. They all suffer from arbitrariness in specifying the size of the difference delta that must be excluded. We propose an alternative Bayesian approach to the design and analysis of active control trials. We derive the posterior probability that E is superior to P or that E is at least k% as good as C and that C is more effective than P. We also derive approximations for use with logistic and proportional hazard models. Selection of prior distributions is discussed, and results are illustrated using data from an active control trial of a drug for the treatment of unstable angina.