Economical Sample Size Calculations for Complex Designs

Music City Center 

In the design of Bayesian clinical trials, the operating characteristics are typically evaluated by estimating the sampling distribution of posterior summaries via Monte Carlo simulation. It is computationally intensive to repeat this estimation process for each design configuration considered, particularly for clustered data that are analyzed using complex, high-dimensional models. We propose an efficient method to assess operating characteristics and determine sample sizes for Bayesian trials with clustered data and multiple endpoints. We prove theoretical results that enable posterior probabilities to be modeled as a function of the sample size. Using these functions, we assess operating characteristics at a range of sample sizes given simulations conducted at only two sample sizes. The applicability of our methodology is illustrated using a current cluster-randomized Bayesian adaptive clinical trial with multiple endpoints.