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of 8
pro vyhledávání: '"Bardo, Maximilian"'
Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or imp
Externí odkaz:
http://arxiv.org/abs/2406.00804
Autor:
Klinglmüller, Florian, Fellinger, Tobias, König, Franz, Friede, Tim, Hooker, Andrew C., Heinzl, Harald, Mittlböck, Martina, Brugger, Jonas, Bardo, Maximilian, Huber, Cynthia, Benda, Norbert, Posch, Martin, Ristl, Robin
While well-established methods for time-to-event data are available when the proportional hazards assumption holds, there is no consensus on the best inferential approach under non-proportional hazards (NPH). However, a wide range of parametric and n
Externí odkaz:
http://arxiv.org/abs/2310.05622
Autor:
Bardo, Maximilian, Huber, Cynthia, Benda, Norbert, Brugger, Jonas, Fellinger, Tobias, Galaune, Vaidotas, Heinz, Judith, Heinzl, Harald, Hooker, Andrew C., Klinglmüller, Florian, König, Franz, Mathes, Tim, Mittlböck, Martina, Posch, Martin, Ristl, Robin, Friede, Tim
For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-pa
Externí odkaz:
http://arxiv.org/abs/2306.16858
Autor:
Bardo, Maximilian, Unkel, Steffen
In statistical models for the analysis of time-to-event data, individual heterogeneity is usually accounted for by means of one or more random effects, also known as frailties. In the vast majority of the literature, the random effect is assumed to f
Externí odkaz:
http://arxiv.org/abs/2303.04915
Akademický článek
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Autor:
Stegherr, Regina, Feifel, Jan, Kuß, Oliver, Unkel, Steffen, Toenges, Gerrit, Antweiler, Kai, Bardo, Maximilian, Ozga, Ann-Kathrin
Publikováno v:
65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS); 20200906-20200909; Berlin; DOCAbstr. 101 /20210226/
Background: The idea of this dataset challenge is that a variety of researchers introduce different approaches for the analysis of time-to-event data applied on the same example-dataset. Ideally, this results in interesting discussions and shows the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a440a57df0d8a4ab196e88dec75bbf03
http://www.egms.de/en/meetings/gmds2020/20gmds122.shtml
http://www.egms.de/en/meetings/gmds2020/20gmds122.shtml
Akademický článek
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Autor:
Bardo M; Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, Göttingen 37073, Germany., Hens N; I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt 3500, Belgium.; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerpen 2610, Belgium., Unkel S; Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, Göttingen 37073, Germany.; Faculty V: School of Life Sciences, University of Siegen, Am Eichenhang 50, Siegen 57076, Germany.
Publikováno v:
Biostatistics (Oxford, England) [Biostatistics] 2024 Sep 10. Date of Electronic Publication: 2024 Sep 10.
