{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T12:03:17Z","timestamp":1792756997030,"version":"4.5.0"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>It is becoming increasingly popular to elicit informative priors on the basis of historical data. Popular existing priors, including the power prior, commensurate prior, and robust meta-analytic predictive prior, provide blanket discounting. Thus, if only a subset of participants in the historical data are exchangeable with the current data, these priors may not be appropriate. In order to combat this issue, propensity score approaches have been proposed. However, these approaches are only concerned with the covariate distribution, whereas exchangeability is typically assessed with parameters pertaining to the outcome. In this paper, we introduce the latent exchangeability prior (LEAP), where observations in the historical data are classified into exchangeable and non-exchangeable groups. The LEAP discounts the historical data by identifying the most relevant subjects from the historical data. We compare our proposed approach against alternative approaches in simulations and present a case study using our proposed prior to augment a control arm in a phase 3 clinical trial in plaque psoriasis with an unbalanced randomization scheme.<\/jats:p>","DOI":"10.1093\/biomtc\/ujae083","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T09:01:21Z","timestamp":1727427681000},"source":"Crossref","is-referenced-by-count":9,"title":["LEAP: the latent exchangeability prior for borrowing information from historical data"],"prefix":"10.1093","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6112-9030","authenticated-orcid":false,"given":"Ethan M","family":"Alt","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of North Carolina at Chapel Hill , Chapel Hill, NC 27516 ,","place":["United 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Amy","family":"Xia","sequence":"additional","affiliation":[{"name":"Amgen Inc., One Amgen Center Drive, Thousand Oaks , CA 91320 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph G","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina at Chapel Hill , Chapel Hill, NC 27516 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"2024092709011479200_bib1","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.3390\/ijerph18042095","article-title":"Handling poor accrual in pediatric trials: a simulation study using a Bayesian approach","volume":"18","author":"Azzolina","year":"2021","journal-title":"International Journal of Environmental Research and Public 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