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Visual Analysis of Missing Values in Longitudinal Cohort Study Data
Author(s) -
Alemzadeh S.,
Niemann U.,
Ittermann T.,
Völzke H.,
Schneider D.,
Spiliopoulou M.,
Bühler K.,
Preim B.
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13662
Subject(s) - missing data , imputation (statistics) , attrition , computer science , dropout (neural networks) , visualization , cohort , statistics , data set , longitudinal data , data visualization , statistical power , data mining , artificial intelligence , machine learning , mathematics , medicine , dentistry
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 v isual analysis of m i ssing v alues i n cohort study d 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.