Bergherr, Elisabeth

[["dc.bibliographiccitation.firstpage","1104-1121"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Biometrical journal. Biometrische Zeitschrift"],["dc.bibliographiccitation.lastpage","1121"],["dc.bibliographiccitation.volume","59"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Taylor-Robinson, David"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Pressler, Tania"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2018-03-13T15:00:13Z"],["dc.date.available","2018-03-13T15:00:13Z"],["dc.date.issued","2017"],["dc.description.abstract","Joint models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modeling. Commonly, joint models are estimated in likelihood-based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and that do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyze the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry that collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models."],["dc.identifier.doi","10.1002/bimj.201600158"],["dc.identifier.pmid","28321912"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13023"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1521-4036"],["dc.title","Boosting joint models for longitudinal and time-to-event data"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","4041736"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.bibliographiccitation.volume","2017"],["dc.contributor.author","Gefeller, Olaf"],["dc.contributor.author","Hofner, Benjamin"],["dc.contributor.author","Mayr, Andreas"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.date.accessioned","2021-09-17T09:10:26Z"],["dc.date.available","2021-09-17T09:10:26Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1155/2017/4041736"],["dc.identifier.pmid","29093744"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89621"],["dc.language.iso","en"],["dc.relation.eissn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.title","Predictive Modelling Based on Statistical Learning in Biomedicine"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","422"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Methods of Information in Medicine"],["dc.bibliographiccitation.lastpage","430"],["dc.bibliographiccitation.volume","55"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Gefeller, Olaf"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2021-09-17T09:11:09Z"],["dc.date.available","2021-09-17T09:11:09Z"],["dc.date.issued","2016-10-17"],["dc.description.abstract","Penalization and regularization techniques for statistical modeling have attracted increasing attention in biomedical research due to their advantages in the presence of high-dimensional data. A special focus lies on algorithms that incorporate automatic variable selection like the least absolute shrinkage operator (lasso) or statistical boosting techniques."],["dc.identifier.doi","10.3414/ME16-01-0033"],["dc.identifier.pmid","27626931"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89629"],["dc.language.iso","en"],["dc.relation.eissn","2511-705X"],["dc.relation.issn","0026-1270"],["dc.title","Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","6083072"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.contributor.author","Mayr, Andreas"],["dc.contributor.author","Hofner, Benjamin"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Meyer, Sebastian"],["dc.contributor.author","Gefeller, Olaf"],["dc.date.accessioned","2021-09-17T09:10:53Z"],["dc.date.available","2021-09-17T09:10:53Z"],["dc.date.issued","2017"],["dc.description.abstract","Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine."],["dc.identifier.doi","10.1155/2017/6083072"],["dc.identifier.pmid","28831290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89626"],["dc.language.iso","en"],["dc.relation.eissn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.title","An Update on Statistical Boosting in Biomedicine"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","0"],["dc.bibliographiccitation.journal","The International Journal of Biostatistics"],["dc.bibliographiccitation.volume","0"],["dc.contributor.author","Rappl, Anja"],["dc.contributor.author","Mayr, Andreas"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.date.accessioned","2022-03-23T09:53:38Z"],["dc.date.available","2022-03-23T09:53:38Z"],["dc.date.issued","2021"],["dc.description.abstract","The development of physical functioning after a caesura in an aged population is still widely unexplored. Analysis of this topic would need to model the longitudinal trajectories of physical functioning and simultaneously take terminal events (deaths) into account. Separate analysis of both results in biased estimates, since it neglects the inherent connection between the two outcomes. Thus, this type of data generating process is best modelled jointly. To facilitate this several software applications were made available. They differ in model formulation, estimation technique (likelihood-based, Bayesian inference, statistical boosting) and a comparison of the different approaches is necessary to identify their capabilities and limitations. Therefore, we compared the performance of the packages JM, joineRML, JMbayes and JMboost of the R software environment with respect to estimation accuracy, variable selection properties and prediction precision. With these findings we then illustrate the topic of physical functioning after a caesura with data from the German ageing survey (DEAS). The results suggest that in smaller data sets and theory driven modelling likelihood-based methods (expectation maximation, JM, joineRML) or Bayesian inference (JMbayes) are preferable, whereas statistical boosting (JMboost) is a better choice with high-dimensional data and data exploration settings."],["dc.identifier.doi","10.1515/ijb-2020-0067"],["dc.identifier.pmid","33818032"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105071"],["dc.language.iso","en"],["dc.relation.eissn","1557-4679"],["dc.relation.issn","1557-4679"],["dc.relation.issn","2194-573X"],["dc.title","More than one way: exploring the capabilities of different estimation approaches to joint models for longitudinal and time-to-event outcomes"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","60"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Methods of Information in Medicine"],["dc.bibliographiccitation.volume","58"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Gefeller, Olaf"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2021-09-17T09:10:17Z"],["dc.date.available","2021-09-17T09:10:17Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1055/s-0038-1669389"],["dc.identifier.pmid","30634196"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89619"],["dc.language.iso","en"],["dc.relation.eissn","2511-705X"],["dc.relation.issn","0026-1270"],["dc.title","Addendum to: Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]