Full metadata record
DC FieldValueLanguage
dc.contributor.authorRichter, Jakob-
dc.contributor.authorFriede, Tim-
dc.contributor.authorRahnenführer, Jörg-
dc.date.accessioned2024-03-06T12:08:13Z-
dc.date.available2024-03-06T12:08:13Z-
dc.date.issued2022-02-25-
dc.identifier.urihttp://hdl.handle.net/2003/42378-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24214-
dc.description.abstractWe propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs, we aim to select the best one in a short time to allow quick decisions. The standard approach to simulate the power for each single design can become too time consuming. When the number of possible designs becomes very large, either large computational resources are required or an exhaustive exploration of all possible designs takes too long. Here, we propose to use BO to quickly find a clinical trial design with high power from a large number of candidate designs. We demonstrate the effectiveness of our approach by optimizing the power of adaptive seamless designs for different sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of all candidate designs shows that BO finds competitive designs in a fraction of the time.en
dc.language.isoende
dc.relation.ispartofseriesBiometrical journal;64(5)-
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/de
dc.subjectadaptive seamless designsen
dc.subjectBayesian optimizationen
dc.subjectclinical trialsen
dc.subjecttreatment selectionen
dc.subject.ddc310-
dc.titleImproving adaptive seamless designs through Bayesian optimizationen
dc.typeTextde
dc.type.publicationtypeResearchArticlede
dcterms.accessRightsopen access-
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/bimj.202000389de
eldorado.secondarypublication.primarycitationRichter, J., Friede, T., & Rahnenführer, J. (2022). Improving adaptive seamless designs through Bayesian optimization. Biometrical Journal, 64, 948–963. https://doi.org/10.1002/bimj.202000389de
Appears in Collections:Statistische Methoden in der Genetik und Chemometrie



This item is protected by original copyright



This item is licensed under a Creative Commons License Creative Commons