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In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post\u2010selection inference problem, which may pose challenges for traditional techniques used for subgroup analysis, such as increased Type I error rates and potential biases from data\u2010driven subgroup identification. In this paper, we discuss two recently developed methods for subgroup selection in regression problems: one based on generalized linear modeling and another on isotonic regression. We demonstrate how these methods can be used for data\u2010driven subgroup identification in the analysis of clinical trials, focusing on two distinct tasks: identifying patient groups that are safe from manifesting adverse events and identifying patient groups with high treatment effect, while controlling for Type I error in both cases. A thorough simulation study is conducted to evaluate the strengths and weaknesses of each method, providing detailed insight into the sensitivity of the Type I error rate control to modeling assumptions.<\/jats:p>","DOI":"10.1002\/sim.70745","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:52:47Z","timestamp":1791960767000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Valid Data\u2010Driven Subgroup Selection With Uniform Guarantees in Clinical Trials"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2891-8440","authenticated-orcid":false,"given":"Manuel M.","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Statistical Laboratory University of Cambridge  Cambridge UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6294-8185","authenticated-orcid":false,"given":"Bj\u00f6rn","family":"Bornkamp","sequence":"additional","affiliation":[{"name":"Novartis Pharma AG  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2008-8340","authenticated-orcid":false,"given":"Frank","family":"Bretz","sequence":"additional","affiliation":[{"name":"Novartis Pharma AG  Basel Switzerland"},{"name":"Medical University of Vienna, Center for Medical Data Science, Institute of Medical Statistics  Vienna Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2111-4168","authenticated-orcid":false,"given":"Timothy I.","family":"Cannings","sequence":"additional","affiliation":[{"name":"School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh  Edinburgh UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4719-0345","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences University of Southampton  Southampton UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-2413","authenticated-orcid":false,"given":"Henry W. 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