Building efficient comparative effectiveness trials through adaptive designs, utility functions, and accrual rate optimization: finding the sweet spot

The time is right for the use of Bayesian Adaptive Designs (BAD) in comparative effectiveness trials. For example, Patient Centered Outcomes Research Institute has joined the Food and Drug Administration and National Intitutes of Health in adopting policies/guidelines encouraging their use. There are multiple aspects to BAD that need to be considered when designing a comparative effectiveness design. First, the adaptation rules can determine the expected size of the trial. Second, a utility function can be used to combine extremely important co-endpoints (e.g., efficacy and tolerability) and is a valuable tool for incorporating clinical expertise and potentially patient preference. Third, accrual rate is also very, very important. Specifically, there is a juxtaposition related to accrual and BAD. If accrual rate is too fast we never gain efficient information for adapting. If accrual rate is too slow we never finish the clinical trial. We propose methodology for finding the 'sweet spot' for BAD that addresses these as design parameters. We demonstrate the methodology on a comparative effectiveness BAD of pharmaceutical agents in cryptogenic sensory polyneuropathy. The study has five arms with two endpoints that are combined with a utility function. The accrual rate is assumed to stem from multiple sites. We perform simulations from which the composite accrual rates across sites result in various piecewise Poisson distributions as parameter inputs. We balance both average number of patients needed and average length of time to finish the study.