Visual Analysis of Missing Values in Longitudinal Cohort Study Data
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2020Pay-Per-View via TIB Hannover:
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Attrition 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.
BibTeX
@article {10.1111:cgf.13662,
journal = {Computer Graphics Forum},
title = {{Visual Analysis of Missing Values in Longitudinal Cohort Study Data}},
author = {Alemzadeh, S. and Niemann, U. and Ittermann, T. and Völzke, H. and Schneider, D. and Spiliopoulou, M. and Bühler, K. and Preim, B.},
year = {2020},
publisher = {© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13662}
}
journal = {Computer Graphics Forum},
title = {{Visual Analysis of Missing Values in Longitudinal Cohort Study Data}},
author = {Alemzadeh, S. and Niemann, U. and Ittermann, T. and Völzke, H. and Schneider, D. and Spiliopoulou, M. and Bühler, K. and Preim, B.},
year = {2020},
publisher = {© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13662}
}