Visual Analytics of Missing Data in Epidemiological Cohort Studies

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dc.contributor.authorAlemzadeh, Shivaen_US
dc.contributor.authorNiemann, Ulien_US
dc.contributor.authorIttermann, Tillen_US
dc.contributor.authorVölzke, Henryen_US
dc.contributor.authorSchneider, Danielen_US
dc.contributor.authorSpiliopoulou, Myraen_US
dc.contributor.authorPreim, Bernharden_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:28Z
dc.date.available2017-09-06T07:12:28Z
dc.date.issued2017
dc.description.abstractWe introduce a visual analytics solution to analyze and treat missing values. Our solution is based on general approaches to handle missing values, but is fine-tuned to the problems in epidemiological cohort study data. The most severe missingness problem in these data is the considerable dropout rate in longitudinal studies that limits the power of statistical analysis and the validity of study findings. Our work is inspired by discussions with epidemiologists and tries to add visual components to their current statistics-based approaches. In this paper we provide a graphical user interface for exploration, imputation and checking the quality of imputations.en_US
dc.description.sectionheadersExploration and Visual Analysis
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20171236
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.pages43-51
dc.identifier.urihttps://doi.org/10.2312/vcbm.20171236
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171236
dc.publisherThe Eurographics Associationen_US
dc.subjectJ.3 [Computer Applications]
dc.subjectLife and Medical Sciences
dc.titleVisual Analytics of Missing Data in Epidemiological Cohort Studiesen_US
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VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine