boinet: An R Package for Bayesian Optimal Interval Design for Dose Finding Based on Both Efficacy and Toxicity Outcomes
Year: 2025
Type: article
Abstract: Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). The R package boinet implements the BOIN-ET design family. The package supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET designs, where users can choose design parameters in flexible and straightforward ways depending on their own application. We demonstrate its capability and effectiveness using several simulation studies.
Source: The R Journal
DOI: 10.32614/rj-2025-012
Authors: Yusuke Yamaguchi, Kentaro Takeda, Kazushi Maruo
Institutions: Astellas Pharma (Japan), University of Tsukuba
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7093081938
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