{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:54:58Z","timestamp":1790675698892,"version":"4.1.0"},"reference-count":22,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmaceutical Statistics"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Conventional dose selection in oncology, which focuses on the maximum tolerated dose (MTD), has certain limitations for targeted agents and immunotherapies, where the efficacy\u2010toxicity relationship may not be monotonic. Regulatory initiatives such as FDA's Project Optimus highlight the need for improved dose optimization methods early in drug development. Although recent Bayesian dose\u2010finding designs enhance flexibility by integrating efficacy data and adopting adaptive monitoring, most depend on binary response outcomes and lack sufficient proof\u2010of\u2010concept (PoC) assessment before advancing doses to confirmatory trials. To address these gaps, we propose BEACON, an extended Bayesian optimization framework for randomized phase II oncology trials. BEACON incorporates time\u2010to\u2010event endpoints into both dose selection and PoC assessment. Extending from the DODII method (Bayesian dose optimization for randomized phase II trials), BEACON combines Bayesian safety and futility monitoring with a pick\u2010the\u2010winner strategy for both survival and binary outcomes. The design dynamically borrows information across dose levels during PoC to enhance decision\u2010making. By allowing both survival and binary outcomes within a unified dose optimization with PoC strategy, BEACON enhances design flexibility and delivers robust confirmation of clinical benefit, aligned with regulatory expectations for dose justification in early\u2010phase oncology studies. Simulation studies demonstrate that the BEACON design exhibits favorable operating characteristics, effectively controlling false go and selection error rates for the recommended doses and reducing the total sample sizes of the trial. With adaptive borrowing across dose levels, the design robustly maintains the desired proof\u2010of\u2010concept power.<\/jats:p>","DOI":"10.1002\/pst.70127","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T08:56:07Z","timestamp":1790672167000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>BEACON<\/scp>\n                    : An Adaptive Bayesian Early\u2010Phase Oncology Trial Design Integrating Dose Optimization and Proof\u2010of\u2010Concept Across Survival and Binary Endpoints"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0332-8380","authenticated-orcid":false,"given":"Yike","family":"Tang","sequence":"first","affiliation":[{"name":"Global Biometrics &amp; Data Sciences Bristol Myers Squibb  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Tian","sequence":"additional","affiliation":[{"name":"Global Biometrics &amp; Data Sciences Bristol Myers Squibb  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9499-6464","authenticated-orcid":false,"given":"Xiaochen","family":"Zhu","sequence":"additional","affiliation":[{"name":"Global Biometrics &amp; Data Sciences Bristol Myers Squibb  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0487-9311","authenticated-orcid":false,"given":"Guotao","family":"Chu","sequence":"additional","affiliation":[{"name":"Global Biometrics &amp; Data Sciences Bristol Myers Squibb  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangang","family":"Liu","sequence":"additional","affiliation":[{"name":"Global Biometrics &amp; Data Sciences Bristol Myers Squibb  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsin\u2010Ju","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Global Biometrics &amp; Data Sciences Bristol Myers Squibb  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu\u2010Pang","family":"Huang","sequence":"additional","affiliation":[{"name":"Global Biometrics &amp; Data Sciences Bristol Myers Squibb  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"issue":"3","key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1177\/1740774514529848","article-title":"Adaptive Designs for Identifying Optimal Biological Dose for Molecularly Targeted Agents","volume":"11","author":"Zang Y.","year":"2014","journal-title":"Clinical Trials"},{"issue":"16","key":"e_1_2_10_3_1","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1056\/NEJMp2109826","article-title":"The Drug\u2010Dosing Conundrum in Oncology\u2014When Less Is More","volume":"385","author":"Shah M.","year":"2021","journal-title":"New England Journal of Medicine"},{"issue":"51","key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"88376","DOI":"10.18632\/oncotarget.19088","article-title":"A Bayesian Pick\u2010The\u2010Winner Design in a Randomized Phase II Clinical Trial","volume":"8","author":"Chen D. 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