{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:58:12Z","timestamp":1781693892062,"version":"3.54.5"},"reference-count":25,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000027","name":"National Institute on Alcohol Abuse and Alcoholism","doi-asserted-by":"publisher","award":["R01AA030045"],"award-info":[{"award-number":["R01AA030045"]}],"id":[{"id":"10.13039\/100000027","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1TR001445"],"award-info":[{"award-number":["UL1TR001445"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stat Methods Med Res"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>An important goal of precision medicine is to personalize medical treatment by identifying individuals who are most likely to benefit from a specific treatment. The likely responder (LR) framework, which identifies a subpopulation where treatment response is expected to exceed a certain clinical threshold, plays a role in this effort. However, the LR framework, and more generally, data-driven subgroup analyses, often fail to account for uncertainty in the estimation of model-based data-driven subgrouping. We propose a simple two-stage approach that integrates subgroup identification with subsequent subgroup-specific inference on treatment effects. We incorporate model estimation uncertainty from the first stage into subgroup-specific treatment effect estimation in the second stage, by utilizing Bayesian posterior distributions from the first stage. We evaluate our method through simulations, demonstrating that the proposed Bayesian two-stage model produces better calibrated confidence intervals than na\u00efve approaches. We apply our method to an international COVID-19 treatment trial, which shows substantial variation in treatment effects across data-driven subgroups.<\/jats:p>","DOI":"10.1177\/09622802261427026","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T14:56:09Z","timestamp":1775660169000},"page":"998-1010","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Bayesian likely responder approach for the analysis of randomized controlled trials"],"prefix":"10.1177","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1665-2153","authenticated-orcid":false,"given":"Annan","family":"Deng","sequence":"first","affiliation":[{"name":"New York University Grossman School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7460-788X","authenticated-orcid":false,"given":"Carole","family":"Siegel","sequence":"additional","affiliation":[{"name":"New York University Grossman School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8994-9583","authenticated-orcid":false,"given":"Hyung G","family":"Park","sequence":"additional","affiliation":[{"name":"New York University Grossman School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Council NR on Earth D Studies L et al. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease 2011."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1037\/cps0000079"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.3322"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/1740774510369350"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cct.2022.106688"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1111\/acer.14414"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/19490976.2023.2178794"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/1741-7015-5-39"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asn004"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9034"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.3651"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8486"},{"key":"e_1_3_3_14_2","unstructured":"Alvarez RM Levin I. Uncertain neighbors: Bayesian propensity score matching for causal inference. arXiv preprint arXiv:210502362 2021."},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.47331"},{"key":"e_1_3_3_16_2","doi-asserted-by":"crossref","unstructured":"Chipman HA George EI McCulloch RE. Bart: Bayesian additive regression trees 2010.","DOI":"10.1214\/09-AOAS285"},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","unstructured":"Rubin DB. Multiple imputation for survey nonresponse 1987.","DOI":"10.1002\/9780470316696"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_3_20_2","first-page":"40","article-title":"Package \u2018xgboost\u2019","volume":"90","author":"Chen T","year":"2019","unstructured":"Chen T, He T, Benesty M, et al. Package \u2018xgboost\u2019. R Version 2019; 90: 40.","journal-title":"R Version"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v097.i01"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30483-7"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.47375"},{"key":"e_1_3_3_24_2","unstructured":"US Food and Drug Administration. Enrichment strategies for clinical trials to support approval of human drugs and biological products\u2014guidance for industry. https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/enrichment-strategies-clinical-trials-support-approval-human-drugs-and-biological-products 2019. FDA guidance document."},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.115.018383"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12944-017-0560-0"}],"container-title":["Statistical Methods in Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802261427026","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09622802261427026","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802261427026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:46:26Z","timestamp":1781693186000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09622802261427026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,8]]},"references-count":25,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1177\/09622802261427026"],"URL":"https:\/\/doi.org\/10.1177\/09622802261427026","relation":{},"ISSN":["0962-2802","1477-0334"],"issn-type":[{"value":"0962-2802","type":"print"},{"value":"1477-0334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,8]]}}}