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Clin Cancer Res. 2016 Sep 1;22(17):4291-301. doi: 10.1158/1078-0432.CCR-16-0592. Epub 2016 Jul 12.

Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

Author information

1
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas. yyuan@mdanderson.org.
2
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
3
Duncan Cancer Center, Baylor College of Medicine, Houston, Texas.
4
Center for Cancer Research, National Cancer Institute, Bethesda, Maryland.

Abstract

Despite more than two decades of publications that offer more innovative model-based designs, the classical 3 + 3 design remains the most dominant phase I trial design in practice. In this article, we introduce a new trial design, the Bayesian optimal interval (BOIN) design. The BOIN design is easy to implement in a way similar to the 3 + 3 design, but is more flexible for choosing the target toxicity rate and cohort size and yields a substantially better performance that is comparable with that of more complex model-based designs. The BOIN design contains the 3 + 3 design and the accelerated titration design as special cases, thus linking it to established phase I approaches. A numerical study shows that the BOIN design generally outperforms the 3 + 3 design and the modified toxicity probability interval (mTPI) design. The BOIN design is more likely than the 3 + 3 design to correctly select the MTD and allocate more patients to the MTD. Compared with the mTPI design, the BOIN design has a substantially lower risk of overdosing patients and generally a higher probability of correctly selecting the MTD. User-friendly software is freely available to facilitate the application of the BOIN design. Clin Cancer Res; 22(17); 4291-301.

PMID:
27407096
PMCID:
PMC5047439
DOI:
10.1158/1078-0432.CCR-16-0592
[Indexed for MEDLINE]
Free PMC Article

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