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.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"]]

[["dc.bibliographiccitation.firstpage","1525"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Clinical Medicine"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Voormolen, Daphne C."],["dc.contributor.author","Zeldovich, Marina"],["dc.contributor.author","Haagsma, Juanita A."],["dc.contributor.author","Polinder, Suzanne"],["dc.contributor.author","Friedrich, Sarah"],["dc.contributor.author","Maas, Andrew I. R."],["dc.contributor.author","Wilson, Lindsay"],["dc.contributor.author","Steyerberg, Ewout W."],["dc.contributor.author","Covic, Amra"],["dc.contributor.author","Andelic, Nada"],["dc.contributor.author","Plass, Anne Marie"],["dc.contributor.author","Wu, Yi-Jhen"],["dc.contributor.author","Asendorf, Thomas"],["dc.contributor.author","von Steinbüechel, Nicole"],["dc.date.accessioned","2021-04-14T08:26:23Z"],["dc.date.available","2021-04-14T08:26:23Z"],["dc.date.issued","2020"],["dc.description.sponsorship","Seventh Framework Programme"],["dc.identifier.doi","10.3390/jcm9051525"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81924"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","MDPI"],["dc.relation.eissn","2077-0383"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Outcomes after Complicated and Uncomplicated Mild Traumatic Brain Injury at Three-and Six-Months Post-Injury: Results from the CENTER-TBI Study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["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"]]

[["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.firstpage","519"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Astronomy and Astrophysics"],["dc.bibliographiccitation.lastpage","523"],["dc.bibliographiccitation.volume","383"],["dc.contributor.author","Jordan, S."],["dc.contributor.author","Friedrich, Sarah"],["dc.date.accessioned","2018-11-07T10:32:03Z"],["dc.date.available","2018-11-07T10:32:03Z"],["dc.date.issued","2002"],["dc.description.abstract","We present highly time resolved circular-polarization and flux spectra of the magnetic white dwarf LP 790-29 taken with the VLT UT1 in order to test the hypothesis that LP 790-29 is a fast rotator with a period of the order of seconds to minutes. Due to low time resolution of former observations this might have been overlooked - leading to the conclusion that LP790-29 has a rotational period of over 100 years. The optical spectrum exhibits one prominent absorption feature with minima at about 4500, 4950, and 5350 Angstrom, which are most likely C-2 Swan-bands shifted by about 180 Angstrom in a magnetic field between 50 MG and 200 MG. At the position of the absorption structures the degree of circular polarization varies between -1% and +1%, whereas it amounts to +8 to +10% in the blue and red continuum. With this very high degree of polarization LP790-29 is very well suited to a search for short time variations, since a variation of several percent in the polarization can be expected for a magnetic field oblique to the rotational axis. From our analysis we conclude that variations on time scales from 50 to 2500 s must have amplitudes less than or similar to0.7% in the continuum and less than or similar to2% in the strongest absorption feature at 4950 Angstrom. While no short-term variations could be found a careful comparison of our polarization data of LP790-29 with those in the literatures indicates significant variations on time scales of decades with a possible period of about 24-28 years."],["dc.identifier.doi","10.1051/0004-6361:20011684"],["dc.identifier.isi","000174184400012"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9724"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/44254"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1432-0746"],["dc.relation.orgunit","Fakultät für Physik"],["dc.title","Search for variations in circular-polarization spectra of the magnetic white dwarf LP 790-29"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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"]]

[["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"]]

[["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"]]