{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T04:05:24Z","timestamp":1793678724494,"version":"4.5.1"},"reference-count":38,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008637","name":"Center for Strategic Scientific Initiatives, National Cancer Institute","doi-asserted-by":"publisher","award":["1R01CA261978"],"award-info":[{"award-number":["1R01CA261978"]}],"id":[{"id":"10.13039\/100008637","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008637","name":"Center for Strategic Scientific Initiatives, National Cancer Institute","doi-asserted-by":"publisher","award":["P30 CA016672"],"award-info":[{"award-number":["P30 CA016672"]}],"id":[{"id":"10.13039\/100008637","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014042","name":"DOD Peer Reviewed Cancer Research Program","doi-asserted-by":"publisher","award":["KC200096P1"],"award-info":[{"award-number":["KC200096P1"]}],"id":[{"id":"10.13039\/100014042","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"national science foundation","doi-asserted-by":"publisher","award":["DMS-2015428"],"award-info":[{"award-number":["DMS-2015428"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clinical Trials"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk\u2013benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and Conclusions:<\/jats:title>\n                    <jats:p>Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy\u2013toxicity risk\u2013benefit trade-offs. The design chooses each patient\u2019s dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/17407745231214750","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T01:53:18Z","timestamp":1702950798000},"page":"287-297","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Risk\u2013benefit trade-offs and precision utilities in phase I-II clinical trials"],"prefix":"10.1177","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6505-8308","authenticated-orcid":false,"given":"Pavlos","family":"Msaouel","sequence":"first","affiliation":[{"name":"Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"name":"Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"name":"David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juhee","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California Santa Cruz, Santa Cruz, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7293-529X","authenticated-orcid":false,"given":"Peter F","family":"Thall","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,12,18]]},"reference":[{"key":"bibr1-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.2786"},{"key":"bibr2-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.abm6420"},{"key":"bibr3-17407745231214750","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13112741"},{"key":"bibr4-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9120"},{"key":"bibr5-17407745231214750","volume-title":"Bayesian designs for phase I-II clinical trials","author":"Yuan Y","year":"2015"},{"key":"bibr6-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdx795"},{"key":"bibr7-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1080\/10543406.2023.2170396"},{"key":"bibr8-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1080\/19466315.2022.2081601"},{"key":"bibr9-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1080\/19466315.2023.2173645"},{"key":"bibr10-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13738"},{"key":"bibr11-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1111\/rssc.12582"},{"key":"bibr12-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29149-5"},{"key":"bibr13-17407745231214750","first-page":"1","volume":"42","author":"Apolo AB","year":"2022","journal-title":"Am Soc Clin Oncol Educ Book"},{"key":"bibr14-17407745231214750","doi-asserted-by":"publisher","DOI":"10.7326\/M18-3667"},{"key":"bibr15-17407745231214750","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14163923"},{"key":"bibr16-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-19425-7"},{"key":"bibr17-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1002\/9781118445112.stat03728"},{"key":"bibr18-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1093\/jncics\/pky006"},{"key":"bibr19-17407745231214750","volume-title":"R: a language and environment for statistical computing","author":"R Core Team","year":"2018"},{"key":"bibr20-17407745231214750","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(12)70559-4"},{"key":"bibr21-17407745231214750","unstructured":"Network NCC. 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