Visual Analytics of Missing Data in Epidemiological Cohort Studies

We 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.