Transitional modeling of experimental longitudinal data with missing values

• Published in: Advances in data analysis and classification Vol. 12; no. 1; pp. 107 - 130
• Main Author: de Rooij, Mark
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2018; Springer Nature B.V
• Subjects: Chemistry and Earth Sciences; Computer Science; Computer simulation; Data Mining and Knowledge Discovery; Economics; Empirical analysis; Finance; Graphical representations; Group dynamics; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Missing data; Modelling; Monte Carlo simulation; Physics; Regression analysis; Regression models; Regular Article; Statistical analysis; Statistical Theory and Methods; Statistics; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Multinomial data; Missing values; Multinomial regression; 62P25; 62-07; 62H30; Longitudinal data; Multidimensional scaling
• ISSN: 1862-5347; 1862-5355
• DOI: 10.1007/s11634-015-0226-6

Longitudinal categorical data are often collected using an experimental design where the interest is in the differential development of the treatment group compared to the control group. Such differential development is often assessed based on average growth curves but can also be based on transitions. For longitudinal multinomial data we describe a transitional methodology for the statistical analysis based on a distance model. Such a distance approach has two advantages compared to a multinomial regression model: (1) sparse data can be handled more efficiently; (2) a graphical representation of the model can be made to enhance interpretation. Within this approach it is possible to jointly model the observations and missing values by adding a new category to the response variable representing the missingness condition. This approach is investigated in a Monte Carlo simulation study. The results show this is a promising way to deal with missing data, although the mechanism is not yet completely understood in all cases. Finally, an empirical example is presented where the advantages of the modeling procedure are highlighted.