Motivated by a real-world drug development program, we propose a Bayesian phase I/II platform design to co-develop therapies with time-to-event efficacy endpoint (BPCT). We jointly model the binary toxicity outcome and the time-to-event efficacy outcome, leveraging a Bayesian hierarchical framework to enable information sharing across indications. At each interim, we update the dose-toxicity and dose-efficacy estimates, as well as the utility for risk–benefit tradeoffs, based on observed data from all indications. This approach informs indication-specific decisions for dose escalation and de-escalation, and identifies the optimal biological dose for each indication. Simulation studies show that the proposed design has desirable operating characteristics, providing a highly flexible and efficient approach for dose optimization. The design has great potential to shorten the drug development timeline, save cost by reducing overlapping infrastructure, and expedite regulatory approval.
Shi et al. (Thu,) studied this question.