Exploring Bayesian adaptive designs in multi-arm randomized controlled trials with a patient preference arm

Clinical trial design efficiency is often defined as a design using the fewest subjects to achieve the trial goal with high probability. Multi-arm trials are more efficient than two-arm trials as they compare multiple therapies in a single trial. There has been an increase in the use of adaptive designs, particularly in response adaptive randomization (RAR) and early stopping, to further improve efficiencies in trial design. Designs with a higher probability of participants receiving better treatment and/or the ability to end early can be more efficient and attractive to investigators, participants, and stakeholders, but come with their criticisms. We set out to find the most efficient design for a future study. The purpose of this motivating study is to identify the application of docosahexaenoic acid (DHA) (control, chew, or patient choice of capsule or chew) that results in the highest adherence among pregnant women. The primary goal of this research was to find the most efficient trial design by comparing the performance of fixed and Bayesian adaptive designs (BAD) through operating characteristics using simulations. To evaluate the performance of competing designs, 'efficiency' was defined through a novel optimality assessment using a data envelopment analysis (DEA). Several fixed and adaptive trial designs with response adaptive randomization (RAR) and early stopping criteria across different accrual rates were considered. The DEA incorporated a balance in power, total sample size, and expected response outcome. The BAD utilizing RAR and early stopping for success and/or futility was determined to be the most efficient study design.