{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T12:00:08Z","timestamp":1780574408097,"version":"3.54.1"},"reference-count":47,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Pharma and Therapeutics"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:p>Clinical trials are primarily conducted to estimate causal effects, but the data collected can also be invaluable for additional research, such as identifying prognostic measures of disease or biomarkers that predict treatment efficacy. However, these exploratory settings are prone to false discoveries (type\u2010I errors) due to the multiple comparisons they entail. Unfortunately, many methods fail to address this issue, in part because the algorithms used are generally designed to optimize predictions and often only provide the measures used for variable selection, such as machine learning model importance scores, as a byproduct. To address the resulting unclear uncertainty in the selection sets, the knockoff framework offers a model\u2010agnostic, robust approach to variable selection with guaranteed type\u2010I error control. Here, we review the knockoff framework in the setting of clinical data, highlighting main considerations using simulation studies. We also extend the framework by introducing a novel knockoff generation method that addresses two main limitations of previously suggested methods relevant for clinical development settings. With this new method, we empirically obtain tighter bounds on type\u2010I error control and gain an order of magnitude in computational efficiency in mixed data settings. We demonstrate comparable selections to those of the competing method for identifying prognostic biomarkers for C\u2010reactive protein levels in patients with psoriatic arthritis in four clinical trials. Our work increases access to the knockoff framework for variable selection from clinical trial data. Hereby, this paper helps to address the current replicability crisis which can result in unnecessary research efforts, increased patient burden, and avoidable costs.<\/jats:p>","DOI":"10.1002\/cpt.3211","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T01:24:05Z","timestamp":1709169845000},"page":"774-785","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["All that Glitters Is not Gold: Type\u2010I Error Controlled Variable Selection from Clinical Trial Data"],"prefix":"10.1002","volume":"115","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3443-2050","authenticated-orcid":false,"given":"Manuela R.","family":"Zimmermann","sequence":"first","affiliation":[{"name":"Novartis Pharma AG  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5618-0667","authenticated-orcid":false,"given":"Mark","family":"Baillie","sequence":"additional","affiliation":[{"name":"Novartis Pharma AG  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6579-5321","authenticated-orcid":false,"given":"Matthias","family":"Kormaksson","sequence":"additional","affiliation":[{"name":"Novartis Pharma AG  Basel Switzerland"}],"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-7453","authenticated-orcid":false,"given":"Konstantinos","family":"Sechidis","sequence":"additional","affiliation":[{"name":"Novartis Pharma AG  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"e_1_2_8_1_1","doi-asserted-by":"crossref","unstructured":"Van Lancker K. Bretz F.&Dukes O.The use of covariate adjustment in randomized controlled trials: An overview arXiv preprint arXiv:2306.05823 (2023).","DOI":"10.1177\/17407745241251568"},{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7064"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djs301"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.k4245"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.h5651"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.0020124"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.294.2.218"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2668"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1337"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12265"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa770"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/cbdd.14262"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8955"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9134"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asy033"},{"key":"e_1_2_8_16_1","unstructured":"Cand\u00e8s E.&Sesia M.Variable selection with knockoffs <https:\/\/web.stanford.edu\/group\/candes\/knockoffs> Accessed December 6 2023."},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1214\/21-AOS2104"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2019.1660174"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2020.1729163"},{"key":"e_1_2_8_20_1","unstructured":"Jordon J. Yoon J.&Van Der Schaar M.Knockoff GAN: generating knockoffs for feature selection using generative adversarial networks.7th International Conference on Learning Representations ICLR 2019(2019) pp. 1\u201325."},{"key":"e_1_2_8_21_1","unstructured":"Kormaksson M. Sechidis K.&Zimmermann M.Knockofftools GitHub repository<https:\/\/github.com\/Novartis\/knockofftools> Accessed December 20 2023."},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2014.900500"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v093.i08"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_8_27_1","first-page":"4768","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17","author":"Scott M.","year":"2017"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2021.1962720"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1214\/16-EJS1129"},{"key":"e_1_2_8_30_1","first-page":"1","article-title":"Derandomised knockoffs: leveraging e\u2010values for false discovery rate control","volume":"00","author":"Ren Z.","year":"2023","journal-title":"J. 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Dai R.&Zheng C.Controlling FDR in selecting group\u2010level simultaneous signals from multiple data sources with application to the national covid collaborative cohort data arXiv preprintarXiv:2303.01599 (2023)."}],"container-title":["Clinical Pharmacology &amp; Therapeutics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpt.3211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T07:22:39Z","timestamp":1754724159000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/10.1002\/cpt.3211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["10.1002\/cpt.3211"],"URL":"https:\/\/doi.org\/10.1002\/cpt.3211","archive":["Portico"],"relation":{},"ISSN":["0009-9236","1532-6535"],"issn-type":[{"value":"0009-9236","type":"print"},{"value":"1532-6535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]},"assertion":[{"value":"2023-09-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}