Thursday, June 2, 2022: Deadline for submissions due 23:59 Anywhere on Earth (AoE) time.We apply these suggested algorithms to a depression trial for purposes of illustration. Sequential MCMC-based algorithms are devised to implement accurate sample size adjustments for multiple interim points. Bayesian models are designed to implement these adjustments in settings where both hard and soft thresholds for distinguishing the presence of treatment effects are present. We devise MCMC-based algorithms to implement sample size adjustments after the first interim point. We show that the uncertainty arising from clinical trials is characterized by filtering later auxiliary parameters through their earlier counterparts and employing the resulting posterior distribution to estimate power.

Our algorithms address each of these shortcomings. We show that current techniques using point estimates of auxiliary parameters for estimating expected power: (i) fail to describe the range of likely power obtained after the anticipated data are observed, (ii) fail to adjust to different kinds of thresholds, and (iii) fail to adjust to the changing patient population. We employ sequential models to describe this evolution. Estimates of future power at later interim points need to track the evolution of the clinical trial. Estimates of expected future power which purport to use this information for purposes of sample size adjustment after given interim points need to reflect this uncertainty. Information before unblinding regarding the success of confirmatory clinical trials is highly uncertain.

Latex for mac poster