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While incorporating NCC can lead to increased power and lower sample sizes, it might introduce bias to the effect estimators if temporal drifts are present. Aiming to mitigate this potential bias, we propose various frequentist model\u2010based approaches that leverage the NCC, while adjusting for time. One of the currently available models incorporates time as a categorical fixed effect, separating the trial duration into periods, defined as time intervals bounded by any arm entering or leaving the platform. In this work, we propose two extensions of this model. First, we consider an alternative definition of time by dividing the trial into fixed\u2010length calendar time intervals. Second, we propose alternative model\u2010based time adjustments. Specifically, we investigate adjusting for random effects and employing splines to model time with a polynomial function. We evaluate the performance of the proposed approaches in a simulation study and illustrate their use through a case study. We show that adjusting for time via a spline function controls the type I error in trials with a sufficiently smooth time trend pattern and may lead to power gains compared to the standard fixed effect model. However, the fixed effect model with period adjustment is the most robust model for arbitrary time trends, provided that the trend is equal across all arms. Especially, in trials with sudden changes in the time trend, the period\u2010adjustment model is preferred if NCCs are\u00a0included.<\/jats:p>","DOI":"10.1002\/bimj.70059","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T11:02:30Z","timestamp":1749726150000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Statistical Modeling to Adjust for Time Trends in Adaptive Platform Trials Utilizing Non\u2010Concurrent Controls"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5727-4270","authenticated-orcid":false,"given":"Pavla","family":"Krotka","sequence":"first","affiliation":[{"name":"Center for Medical Data Science Medical University of Vienna Vienna Austria"},{"name":"Department of Statistics and Operations Research Universitat Polit\u00e8cnica de Catalunya Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Posch","sequence":"additional","affiliation":[{"name":"Center for Medical Data Science Medical University of Vienna Vienna Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Gewily","sequence":"additional","affiliation":[{"name":"Department of Pharmacy Uppsala University Uppsala Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00fcnter","family":"H\u00f6glinger","sequence":"additional","affiliation":[{"name":"Department of Neurology LMU University Hospital, Ludwig\u2010Maximilians\u2010Universit\u00e4t (LMU) M\u00fcnchen Munich Germany"},{"name":"German Center for Neurodegenerative Diseases (DZNE) Munich Germany"},{"name":"Munich Cluster for Systems Neurology (SyNergy) Munich Germany"},{"name":"Department of Neurology Hannover Medical School Hanover Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4400-7541","authenticated-orcid":false,"given":"Marta","family":"Bofill Roig","sequence":"additional","affiliation":[{"name":"Center for Medical Data Science Medical University of Vienna Vienna Austria"},{"name":"Department of Statistics and Operations Research Universitat Polit\u00e8cnica de Catalunya Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8019"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13063-023-07398-7"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/17407745221112016"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-022-01683-w"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/pst.2120"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.202200305"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMc2102446"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43441-023-00585-3"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1038425655"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awm032"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(23)00144-4"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2011.01564.x"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(20)30489-0"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13063-020-04971-2"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/10543406.2019.1657132"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2023.102384"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201800395"},{"key":"e_1_2_13_19_1","doi-asserted-by":"crossref","unstructured":"Krotka P. 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