An efficient Bayesian platform trial design for borrowing adaptively from historical control data in lymphoma

To reduce a clinical trial's cost and ethical risk to its enrollees, some oncology trial designers have suggested borrowing information from similar but already completed trials to reduce the number of patients needed for the current study. Motivated by competing drug therapies for lymphoma, we propose a Bayesian adaptive "platform" trial design that uses commensurate prior methods at interim analyses to borrow adaptively from the control group of an earlier-starting trial. The design adjusts the trial's randomization ratio in favor of the novel treatment when the interim posterior indicates commensurability of the two control groups. In this setting, our design can supplement a control arm with historical data, and randomize more new patients to the novel treatments. This design is both ethical and economical, since it shortens the process of introducing new treatments into the market, and any additional costs introduced by this design will be compensated by the savings in control arm sizes. Our approach performs well via simulation across settings with varying degrees of commensurability and true treatment effects, and compares favorably to an adaptive "all-or-nothing" approach in which the decision to pool or discard historical controls is based on a simple ad-hoc frequentist test at interim analysis. We also consider a three drug extension where a new imaginary intervention joins the platform, and show again that our procedure performs well via simulation.