Friedrich, Sarah

[["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"]]

[["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"]]

[["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"]]

[["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"]]