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Power and sample size analysis comprises a critical component of clinicaltrial study design. There is an extensive collection of methods addressing thisproblem from diverse perspectives. The Bayesian paradigm, in particular, hasattracted noticeable attention and includes different perspectives for samplesize determination. Building upon a cost-effectiveness analysis undertaken byO'Hagan and Stevens (2001) with different priors in the design and analysisstage, we develop a general Bayesian framework for simulation-based sample sizedetermination that can be easily implemented on modest computing architectures.We further qualify the need for different priors for the design and analysisstage. We work primarily in the context of conjugate Bayesian linear regressionmodels, where we consider the situation with known and unknown variances.Throughout, we draw parallels with frequentist solutions, which arise asspecial cases, and alternate Bayesian approaches with an emphasis on how thenumerical results from existing methods arise as special cases in ourframework.

A Unifying Bayesian Approach for Sample Size Determination Using Design and Analysis Priors