TITE‐gBOIN: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding accounting for toxicity grades

The new therapeutic agents, such as molecular targeted agents and immuno‐oncology therapies, appear more likely to induce multiple toxicities at different grades than dose‐limiting toxicities defined in traditional dose‐finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de‐escalation on time because of the fast accrual rate and/or the late‐onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time‐to‐event Bayesian optimal interval design to accelerate the dose‐finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named “TITE‐gBOIN” design, is a nonparametric and model‐assisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose‐finding trials. A simulation study shows that the TITE‐gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early‐stage dose‐finding trials.