Now showing 1 - 10 of 10
  • 2017Journal Article
    [["dc.bibliographiccitation.firstpage","255"],["dc.bibliographiccitation.journal","Journal of Multivariate Analysis"],["dc.bibliographiccitation.lastpage","265"],["dc.bibliographiccitation.volume","153"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Pauly, Markus"],["dc.date.accessioned","2018-11-07T10:29:34Z"],["dc.date.available","2018-11-07T10:29:34Z"],["dc.date.issued","2017"],["dc.description.abstract","For general repeated measures designs the Wald-type statistic (WTS) is an asymptotically valid procedure allowing for unequal covariance matrices and possibly non-normal multivariate observations. The drawback of this procedure is its poor performance for small to moderate samples, i.e., decisions based on the WTS may become quite liberal. It is the aim of the present paper to improve the small-sample behavior of the WTS by means of a novel permutation procedure. In particular, it is shown that a permutation version of the WTS inherits its good large-sample properties while yielding a very accurate finite-sample control of the type-I error as shown in extensive simulations. Moreover, the new permutation method is motivated by a practical data set of a split plot design with a factorial structure on the repeated measures. (C) 2016 Elsevier Inc. All rights reserved."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft [DFG-PA 2409/3-1]"],["dc.identifier.doi","10.1016/j.jmva.2016.10.004"],["dc.identifier.isi","000389867100016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43665"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Elsevier Inc"],["dc.relation.issn","0047-259X"],["dc.title","Permuting longitudinal data in spite of the dependencies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]
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  • 2020Journal Article
    [["dc.bibliographiccitation.firstpage","106213"],["dc.bibliographiccitation.journal","Contemporary Clinical Trials"],["dc.bibliographiccitation.volume","99"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2021-04-14T08:30:07Z"],["dc.date.available","2021-04-14T08:30:07Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1016/j.cct.2020.106213"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83115"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.issn","1551-7144"],["dc.title","Causal inference methods for small non-randomized studies: Methods and an application in COVID-19"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2020Journal Article
    [["dc.bibliographiccitation.issue","S9"],["dc.bibliographiccitation.journal","Alzheimer's & Dementia"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Ballard, Clive"],["dc.contributor.author","Nørgaard, Caroline Holm"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Mørch, Lina Steinrud"],["dc.contributor.author","Gerds, Thomas"],["dc.contributor.author","Møller, Daniel Vega"],["dc.contributor.author","Knudsen, Lotte Bjerre"],["dc.contributor.author","Kvist, Kajsa"],["dc.contributor.author","Zinman, Bernard"],["dc.contributor.author","Holm, Ellen"],["dc.contributor.author","Hansen, Charlotte Thim"],["dc.date.accessioned","2021-12-08T12:29:42Z"],["dc.date.available","2021-12-08T12:29:42Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1002/alz.042909"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/96176"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-476"],["dc.relation.eissn","1552-5279"],["dc.relation.issn","1552-5260"],["dc.rights.uri","http://onlinelibrary.wiley.com/termsAndConditions#vor"],["dc.title","Liraglutide and semaglutide: Pooled post hoc analysis to evaluate risk of dementia in patients with type 2 diabetes"],["dc.title.alternative","Human/Human trials: Other"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2022Journal Article
    [["dc.bibliographiccitation.journal","Advances in Statistical Analysis"],["dc.contributor.author","Jahn, Beate"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Behnke, Joachim"],["dc.contributor.author","Engel, Joachim"],["dc.contributor.author","Garczarek, Ursula"],["dc.contributor.author","Münnich, Ralf"],["dc.contributor.author","Pauly, Markus"],["dc.contributor.author","Wilhelm, Adalbert"],["dc.contributor.author","Wolkenhauer, Olaf"],["dc.contributor.author","Zwick, Markus"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2022-05-02T08:09:42Z"],["dc.date.available","2022-05-02T08:09:42Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings."],["dc.description.abstract","Abstract A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings."],["dc.identifier.doi","10.1007/s10182-022-00439-7"],["dc.identifier.pii","439"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/107442"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-561"],["dc.relation.eissn","1863-818X"],["dc.relation.issn","1863-8171"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","On the role of data, statistics and decisions in a pandemic"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2021Journal Article
    [["dc.bibliographiccitation.journal","Advances in data analysis and classification"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Antes, Gerd"],["dc.contributor.author","Behr, Sigrid"],["dc.contributor.author","Binder, Harald"],["dc.contributor.author","Brannath, Werner"],["dc.contributor.author","Dumpert, Florian"],["dc.contributor.author","Ickstadt, Katja"],["dc.contributor.author","Kestler, Hans A."],["dc.contributor.author","Lederer, Johannes"],["dc.contributor.author","Leitgöb, Heinz"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2021-09-01T06:42:38Z"],["dc.date.available","2021-09-01T06:42:38Z"],["dc.date.issued","2021"],["dc.description.abstract","The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching."],["dc.identifier.doi","10.1007/s11634-021-00455-6"],["dc.identifier.pii","455"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89107"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.relation.eissn","1862-5355"],["dc.relation.issn","1862-5347"],["dc.title","Is there a role for statistics in artificial intelligence?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2022Journal Article
    [["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Alzheimer’s & Dementia: Translational Research & Clinical Interventions"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Nørgaard, Caroline Holm"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Hansen, Charlotte Thim"],["dc.contributor.author","Gerds, Thomas"],["dc.contributor.author","Ballard, Clive"],["dc.contributor.author","Møller, Daniel Vega"],["dc.contributor.author","Knudsen, Lotte Bjerre"],["dc.contributor.author","Kvist, Kajsa"],["dc.contributor.author","Zinman, Bernard"],["dc.contributor.author","Holm, Ellen"],["dc.contributor.author","Mørch, Lina Steinrud"],["dc.date.accessioned","2022-04-01T10:01:30Z"],["dc.date.available","2022-04-01T10:01:30Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1002/trc2.12268"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105678"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.eissn","2352-8737"],["dc.relation.issn","2352-8737"],["dc.title","Treatment with glucagon‐like peptide‐1 receptor agonists and incidence of dementia: Data from pooled double‐blind randomized controlled trials and nationwide disease and prescription registers"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2022Journal Article
    [["dc.bibliographiccitation.journal","AStA Advances in Statistical Analysis"],["dc.contributor.author","Jahn, Beate"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Behnke, Joachim"],["dc.contributor.author","Engel, Joachim"],["dc.contributor.author","Garczarek, Ursula"],["dc.contributor.author","Münnich, Ralf"],["dc.contributor.author","Pauly, Markus"],["dc.contributor.author","Wilhelm, Adalbert"],["dc.contributor.author","Wolkenhauer, Olaf"],["dc.contributor.author","Zwick, Markus"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2022-09-01T09:49:19Z"],["dc.date.available","2022-09-01T09:49:19Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1007/s10182-022-00460-w"],["dc.identifier.pii","460"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113390"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-597"],["dc.relation.eissn","1863-818X"],["dc.relation.issn","1863-8171"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Authors’ response: on the role of data, statistics and decisions in a pandemic"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2022Journal Article
    [["dc.bibliographiccitation.artnumber","096228022211335"],["dc.bibliographiccitation.journal","Statistical Methods in Medical Research"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Groll, Andreas"],["dc.contributor.author","Ickstadt, Katja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Pauly, Markus"],["dc.contributor.author","Rahnenführer, Jörg"],["dc.contributor.author","Friede, Tim"],["dc.date.accessioned","2022-12-01T08:31:20Z"],["dc.date.available","2022-12-01T08:31:20Z"],["dc.date.issued","2022"],["dc.description.abstract","A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources."],["dc.description.sponsorship"," Volkswagen Foundation https://doi.org/10.13039/501100001663"],["dc.description.sponsorship"," Deutsche Forschungsgemeinschaft https://doi.org/10.13039/501100001659"],["dc.identifier.doi","10.1177/09622802221133557"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118143"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.relation.eissn","1477-0334"],["dc.relation.issn","0962-2802"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Regularization approaches in clinical biostatistics: A review of methods and their applications"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2020Journal Article
    [["dc.bibliographiccitation.firstpage","2209"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Journal of Statistical Computation and Simulation"],["dc.bibliographiccitation.lastpage","2227"],["dc.bibliographiccitation.volume","90"],["dc.contributor.author","Ditzhaus, Marc"],["dc.contributor.author","Friedrich, Sarah"],["dc.date.accessioned","2021-04-14T08:25:37Z"],["dc.date.available","2021-04-14T08:25:37Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1080/00949655.2020.1773463"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81687"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1563-5163"],["dc.relation.issn","0094-9655"],["dc.title","More powerful logrank permutation tests for two-sample survival data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]
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  • 2013Journal Article
    [["dc.bibliographiccitation.artnumber","132"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","The European Physical Journal A"],["dc.bibliographiccitation.volume","49"],["dc.contributor.author","Thiel, M."],["dc.contributor.author","Metag, V."],["dc.contributor.author","Bartolome, P. Aguar"],["dc.contributor.author","Akasoy, L. K."],["dc.contributor.author","Annand, J. R. M."],["dc.contributor.author","Arends, H. J."],["dc.contributor.author","Bantawa, K."],["dc.contributor.author","Beck, R."],["dc.contributor.author","Bekrenev, V."],["dc.contributor.author","Berghaeuser, H."],["dc.contributor.author","Braghieri, A."],["dc.contributor.author","Briscoe, W. J."],["dc.contributor.author","Brudvik, J."],["dc.contributor.author","Cherepnya, S."],["dc.contributor.author","Dieterle, M."],["dc.contributor.author","Downie, E. J."],["dc.contributor.author","Drexler, P."],["dc.contributor.author","Fil'kov, L. V."],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Glazier, D. I."],["dc.contributor.author","Gregor, R."],["dc.contributor.author","Heid, E."],["dc.contributor.author","Hornidge, D."],["dc.contributor.author","Jahn, Olaf"],["dc.contributor.author","Jude, T. C."],["dc.contributor.author","Kashevarov, V. L."],["dc.contributor.author","Keshelashvili, I."],["dc.contributor.author","Kondratiev, R."],["dc.contributor.author","Korolija, M."],["dc.contributor.author","Kotulla, M."],["dc.contributor.author","Krusche, B."],["dc.contributor.author","Kulbardis, A."],["dc.contributor.author","Lemmer, Boris"],["dc.contributor.author","Lisin, V."],["dc.contributor.author","Livingston, K."],["dc.contributor.author","Lugert, S."],["dc.contributor.author","MacGregor, I. J. D."],["dc.contributor.author","Maghrbi, Y."],["dc.contributor.author","Mancell, J."],["dc.contributor.author","Manley, D. M."],["dc.contributor.author","Martinez, M."],["dc.contributor.author","McGeorge, J. C."],["dc.contributor.author","McNicoll, E. F."],["dc.contributor.author","Middleton, D. G."],["dc.contributor.author","Mushkarenkov, A."],["dc.contributor.author","Nanova, M."],["dc.contributor.author","Nefkens, B. M. K."],["dc.contributor.author","Nikolaev, A."],["dc.contributor.author","Novotny, R. W."],["dc.contributor.author","Ostrick, M."],["dc.contributor.author","Pedroni, P."],["dc.contributor.author","Pheron, F."],["dc.contributor.author","Polonski, A."],["dc.contributor.author","Prakhov, S. N."],["dc.contributor.author","Robinson, J."],["dc.contributor.author","Rosner, G."],["dc.contributor.author","Rostomyan, T."],["dc.contributor.author","Schumann, Steffen"],["dc.contributor.author","Sikora, M. H."],["dc.contributor.author","Starostin, A."],["dc.contributor.author","Supek, I."],["dc.contributor.author","Thomas, Andy"],["dc.contributor.author","Unverzagt, M."],["dc.contributor.author","Watts, D. P."],["dc.contributor.author","Weil, J."],["dc.contributor.author","Werthmueller, D."],["dc.contributor.author","Zamboni, I."],["dc.date.accessioned","2018-11-07T09:18:30Z"],["dc.date.available","2018-11-07T09:18:30Z"],["dc.date.issued","2013"],["dc.description.abstract","The in-medium properties of the omega meson were investigated at the MAMI-C electron accelerator using the Crystal Ball/TAPS detector system to identify omega mesons through their omega -> pi(0)gamma decay channel. Two solid targets (C, Nb) and a LH2 target as a reference have been used. In the energy range 900 to 1300 MeV the omega lineshapes for the different nuclei are compared and a broadening of the omega signal for the niobium target is observed in comparison to the liquid-hydrogen target. In a second analysis the omega momentum distributions for the solid targets are determined for 50 MeV/c wide bins. For both analyses a comparison to transport calculations shows that the data are consistent with an in-medium modification by collisional broadening, as observed by transparency ratio measurements."],["dc.identifier.doi","10.1140/epja/i2013-13132-6"],["dc.identifier.isi","000325828100001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28428"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1434-601X"],["dc.relation.issn","1434-6001"],["dc.title","In-medium modifications of the omega meson near the production threshold"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]
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