Evaluating the Quality of Data Imputation in Cardiovascular Risk Studies Through the Dissimilarity Profile Analysis

Missing data handling is one of the crucial problems in statistical analyses, and almost always is overcome by imputation. Although the literature is rich in different imputation approaches, the problem of the assessment of the quality of imputation, i.e., appraising whether the imputed values or categories are plausible for variables and units, seems to have received less attention. This issue is critical in every field of application, such as the medical context considered here, i.e., the assessment of cardiovascular disease risks. We faced the problem of comparing the results obtained with different imputation methods and assessing the quality of imputation through the dissimilarity profile analysis (DPA), which is a multivariate exploratory method for the analysis of dissimilarity matrices. We also combined DPA with the traditional profile analysis for data matrices in order to improve understanding of the differentiation components among imputation methods.