Single Agent
Backfill
Late-onset
Combination
Optimal Biological Dose (OBD)
BOIN/iBOIN
Find MTD for single-agent
trials
BOIN is a novel model-assisted phase-1 trial
design that is as easy to implement as the 3+3
design,but yields superior performance compared
to more
complicated model-based designs, such as CRM.
BF-BOIN / BARD
Backfill Bayesian Optimal Interval Design for Phase I Clinical Trials
Backfilling BOIN (BF-BOIN) enables the simultaneous allocation of additional patients to doses deemed safe and demonstrating promising outcomes when employing BOIN for dose escalation. This approach facilitates the enrollment of more patients across multiple doses without extending the trial duration. It contributes to informing dose selection and optimization, aligning seamlessly with FDA Project Optimus.
TITE-BOIN
Find MTD in trials with late-onset toxicity
or fast accrual
Time-to-Event BOIN (TITE-BOIN) allows for
real-time dose assignment for new patients while
some enrolled patients’ toxicity data are still
pending, thereby
significantly shortening the trial duration. It
is as easy to implement as the rolling 6 design,
but yields much better performance.
BOIN Comb
Find MTD or MTD contour for combination
trials
BOIN Comb handles combinations of two drugs,
each with multiple dose levels.
It is as easy to implement as the 3+3 design,
but yields superior perfomance compared to
more complicated model-based designs.
U-BOIN
A two-stage design to find OBD for
targeted and immune therapy
U-BOIN is a utility-based seamless Bayesian
phase I/II trial design to find the optimal
biological dose (OBD) for targeted and immune
therapies. It allows physicians to incorporate
the risk-benefit trade-off to more realistically
reflect the clinical practice.
BOIN12 / TITE-BOIN12
A single-stage design to find OBD for targeted and immune therapies
BOIN12 is a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit tradeoff. It makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity, and adaptively allocates patients to the dose that optimizes the toxicity-efficacy tradeoff.