Visual Analysis of Missing Values in Longitudinal Cohort Study Data

dc.contributor.authorAlemzadeh, S.en_US
dc.contributor.authorNiemann, U.en_US
dc.contributor.authorIttermann, T.en_US
dc.contributor.authorVölzke, H.en_US
dc.contributor.authorSchneider, D.en_US
dc.contributor.authorSpiliopoulou, M.en_US
dc.contributor.authorBühler, K.en_US
dc.contributor.authorPreim, B.en_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-05-22T12:24:39Z
dc.date.available2020-05-22T12:24:39Z
dc.date.issued2020
dc.description.abstractAttrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow‐up examinations. Dropouts result in biased data and cause the reduction of 1ata set size. Moreover, they limit the power of statistical analysis and the validity of study findings. Visualization can play a strong role in analysing and displaying the missingness patterns. In this work, we present VIVID, a framework for the isual analysis of mssing alues n cohort study ata. VIVID is inspired by discussions with epidemiologists and adds visual components to their current statistics‐based approaches. VIVID provides functions for exploration, imputation and validity check of imputations. The main focus of this paper is multiple imputation to fix the missing data.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.13662
dc.identifier.issn1467-8659
dc.identifier.pages63-75
dc.identifier.urihttps://doi.org/10.1111/cgf.13662
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13662
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectvisual analytics
dc.subjectinformation visualization
dc.subjectJ.3 [Computer Applications]: Life and Medical Sciences—
dc.titleVisual Analysis of Missing Values in Longitudinal Cohort Study Dataen_US
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