{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:54:03Z","timestamp":1786978443988,"version":"build-2736575974"},"reference-count":45,"publisher":"Wiley","issue":"14","license":[{"start":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T00:00:00Z","timestamp":1619308800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>Knockoffs provide a general framework for controlling the false discovery rate when performing variable selection. Much of the Knockoffs literature focuses on theoretical challenges and we recognize a need for bringing some of the current ideas into practice. In this paper we propose a sequential algorithm for generating knockoffs when underlying data consists of both continuous and categorical (factor) variables. Further, we present a heuristic multiple knockoffs approach that offers a practical assessment of how robust the knockoff selection process is for a given dataset. We conduct extensive simulations to validate performance of the proposed methodology. Finally, we demonstrate the utility of the methods on a large clinical data pool of more than 2000 patients with psoriatic arthritis evaluated in four clinical trials with an IL\u201017A inhibitor, secukinumab (Cosentyx), where we determine prognostic factors of a well established clinical outcome. The analyses presented in this paper could provide a wide range of applications to commonly encountered datasets in medical practice and other fields where variable selection is of particular interest.<\/jats:p>","DOI":"10.1002\/sim.8955","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T02:29:06Z","timestamp":1619404146000},"page":"3313-3328","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Sequential knockoffs for continuous and categorical predictors: With application to a large psoriatic arthritis clinical trial pool"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6579-5321","authenticated-orcid":false,"given":"Matthias","family":"Kormaksson","sequence":"first","affiliation":[{"name":"Novartis Pharmaceuticals Corporation East Hanover New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4653-9303","authenticated-orcid":false,"given":"Luke J.","family":"Kelly","sequence":"additional","affiliation":[{"name":"Oxford University Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Novartis Pharmaceuticals Corporation East Hanover New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sibylle","family":"Haemmerle","sequence":"additional","affiliation":[{"name":"Novartis Pharmaceuticals Corporation Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luminita","family":"Pricop","sequence":"additional","affiliation":[{"name":"Novartis Pharmaceuticals Corporation East Hanover New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ohlssen","sequence":"additional","affiliation":[{"name":"Novartis Pharmaceuticals Corporation East Hanover New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,4,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2016.1154108"},{"issue":"1","key":"e_1_2_8_3_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini Y","year":"1995","journal-title":"J Royal Stat Soc Ser B"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.6.509"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1027"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/07-STS236"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013699998"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3462-1"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2015.1108848"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"issue":"1","key":"e_1_2_8_11_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the Lasso","volume":"58","author":"Tibshirani R","year":"1996","journal-title":"J Royal Stat Soc Ser B (Methodol)"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOS1521"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1371"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOS798"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1534\/genetics.114.167817"},{"issue":"79","key":"e_1_2_8_17_1","first-page":"1","article-title":"Feature selection methods and genomic big data: a systematic review","volume":"6","author":"Tadist K","year":"2019","journal-title":"J Big Data"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720005001004"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1337"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12265"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2019.1660174"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asy033"},{"key":"e_1_2_8_23_1","unstructured":"PattersonE SesiaM. knockoff: the knockoff filter for controlled variable selection. 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