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Given the cost and duration of phase III and phase IV clinical trials, thedevelopment of statistical methods for go/no-go decisions is vital. In thispaper, we introduce a Bayesian methodology to compute the probability ofsuccess based on the current data of a treatment regimen for the multivariatelinear model. Our approach utilizes a Bayesian seemingly unrelated regressionmodel, which allows for multiple endpoints to be modeled jointly even if thecovariates between the endpoints are different. Correlations between endpointsare explicitly modeled. This Bayesian joint modeling approach unifies singleand multiple testing procedures under a single framework. We develop anapproach to multiple testing that asymptotically guarantees strict family-wiseerror rate control, and is more powerful than frequentist approaches tomultiplicity. The method effectively yields those of Ibrahim et al. andChuang-Stein as special cases, and, to our knowledge, is the only method thatallows for robust sample size determination for multiple endpoints and/orhypotheses and the only method that provides strict family-wise type I errorcontrol in the presence of multiplicity.

Bayesian Multivariate Probability of Success Using Historical Data with Strict Control of Family-wise Error Rate