2014/12/05 by Suyu Liu, Ying Yuan · 1 citation
Computer Science · Decision Sciences · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Optimal Experimental Design Methods #Statistical Methods in Clinical Trials
paper · doi:10.1111/rssc.12089
crossref issued 2014/12/05 · crossref published 2014/12/05 · crossref published-online 2014/12/05 · openalex publication_date 2014/12/05 · crossref created 2014/12/09 · crossref published-print 2015/04/01 · crossref deposited 2023/01/03 · openalex created_date 2025/10/10 · crossref indexed 2026/07/27 · openalex updated_date 2026/07/28
In phase I trials, effectively treating patients and minimizing the chance of exposing them to subtherapeutic and overly toxic doses are clinicians' top priority. Motived by this practical consideration, we propose Bayesian optimal interval (BOIN) designs to find the maximum tolerated dose and to minimize the probability of inappropriate dose assignments for patients. We show, both theoretically and numerically, that the BOIN design not only has superior finite and large sample properties but also can be easily implemented in a simple way similar to the traditional ‘3+3’ design. Compared with the well-known continual reassessment method, the BOIN design yields comparable average performance to select the maximum tolerated dose but has a substantially lower risk of assigning patients to subtherapeutic and overly toxic doses. We apply the BOIN design to two cancer clinical trials.