{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:03:04Z","timestamp":1789653784323,"version":"4.0.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["TL1TR001875"],"award-info":[{"award-number":["TL1TR001875"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UL1TR001873"],"award-info":[{"award-number":["UL1TR001873"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Project Optimus, an initiative by the FDA\u2019s Oncology Center of Excellence, seeks to reform the dose-optimization and dose-selection paradigm in oncology. We propose a dose-optimization design that considers plateau efficacy profiles, integrates pharmacokinetic data to inform the exposure-toxicity curve, and accounts for patient characteristics that may contribute to heterogeneity in response. The dose-optimization design is carried out in two stages. First, a toxicity-driven stage estimates a safe set of doses. Then, a dose-ranging efficacy-driven stage explores the set using response and patient characteristic data, employing Bayesian Sparse Group Selection to understand patient heterogeneity. Between stages, the design integrates pharmacokinetic data and uses futility assessments to identify the target population among the general phase I patient population. An optimal dose is recommended for each identified subpopulation within the target population. The simulation study demonstrates that a model-based approach to identifying the target population can be effective; patient characteristics relating to heterogeneity were identified, and different optimal doses were recommended for each identified target subpopulation. Most designs that account for patient heterogeneity are intended for trials where heterogeneity is known, and pre-defined subpopulations are specified. However, given the limited information at such an early stage, subpopulations should be learned through the design.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag161","type":"journal-article","created":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T13:52:29Z","timestamp":1788616349000},"source":"Crossref","is-referenced-by-count":0,"title":["Seamless dose optimization design accounting for unknown patient heterogeneity in cancer clinical trials"],"prefix":"10.1093","volume":"82","author":[{"given":"Rebecca B","family":"Silva","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Columbia University Mailman School of Public Health , New York, NY 10032 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Columbia University Mailman School of Public Health , New York, NY 10032 ,","place":["United 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