{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:48:07Z","timestamp":1791892087715,"version":"4.3.4"},"reference-count":23,"publisher":"Wiley","issue":"23-24","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"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":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    In clinical trials, it is critical to incorporate prognostic variables to improve precision for estimating treatment effects. Per FDA guidance, these prognostic variables need to be prospectively specified in the statistical analysis plan. Therefore, there is a great need to effectively identify prognostic variables from historical studies that are similar to the new study. In this work, we propose a multi\u2010task learning method to identify prognostic variables from multiple related historical clinical trials. Specifically, a\n                    <jats:italic>bi\u2010level constraint variable selection<\/jats:italic>\n                    (\n                    <jats:italic>Bi\u2010CVS<\/jats:italic>\n                    ) method is developed to identify both study\u2010specific (within\u2010study) and program\u2010level (cross\u2010study) prognostic variables. In addition, we develop an efficient algorithm for the\n                    <jats:italic>Bi\u2010CVS<\/jats:italic>\n                    , tailored for clinical trial settings, and investigate its consistency property. The performance of the proposed method is evaluated through simulations for various endpoints, including continuous, binary, and time\u2010to\u2010event. A real clinical application is also presented to illustrate the proposed method.\n                  <\/jats:p>","DOI":"10.1002\/sim.70772","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T10:54:55Z","timestamp":1791888895000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi\u2010Task Learning Approach for Identifying Prognostic Variables Across Multiple Historical Clinical Trials"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0874-7970","authenticated-orcid":false,"given":"Xueying","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Statistical Science Baylor University  Waco Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijing","family":"Jiang","sequence":"additional","affiliation":[{"name":"Data and Quantitative Sciences Bristol Myers Squibb  Madison New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Data and Quantitative Sciences Bristol Myers Squibb  Madison New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1560-2405","authenticated-orcid":false,"given":"Xinwei","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Statistics Virginia Tech  Blacksburg Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"US Food and Drug Administration \u201cAdjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products \u201d 2023."},{"issue":"4","key":"e_1_2_10_3_1","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/S0197-2456(00)00061-1","article-title":"How to Select Covariates to Include in the Analysis of a Clinical Trial","volume":"21","author":"Raab G. 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