Testing and Visualization of Associations in Three-Way Contingency Tables: A Study of the Gender Gap in Patients with Type 1 Diabetes and Cardiovascular Complications

Rosaria Lombardo (), Eric J. Beh, Francesco Prattichizzo, Giuseppe Lucisano, Antonio Nicolucci, Björn Eliasson, Hanne Krage Carlsen, Rosalba La Grotta, Valeria Pellegrini and Antonio Ceriello
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Rosaria Lombardo: Department of Economics, University of Campania “Luigi Vanvitelli”, 81043 Capua (CE), Italy
Eric J. Beh: National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong, Wollongong, NSW 2522, Australia
Francesco Prattichizzo: IRCCS MultiMedica, 20138 Milan, Italy
Giuseppe Lucisano: CORESEARCH, Center for Outcomes Research and Clinical Epidemiology, 65122 Pescara, Italy
Antonio Nicolucci: CORESEARCH, Center for Outcomes Research and Clinical Epidemiology, 65122 Pescara, Italy
Björn Eliasson: Department of Medicine, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
Hanne Krage Carlsen: Center of Registers in Region Västra Götaland, 413 45 Gothenburg, Sweden
Rosalba La Grotta: IRCCS MultiMedica, 20138 Milan, Italy
Valeria Pellegrini: IRCCS MultiMedica, 20138 Milan, Italy
Antonio Ceriello: IRCCS MultiMedica, 20138 Milan, Italy

Mathematics, 2024, vol. 12, issue 14, 1-13

Abstract: Using data from the Swedish National Diabetes Register, this study examines the gender disparity among patients with type 1 diabetes who have experienced a specific cardiovascular complication, while exploring the association between their weight variability, age group, and gender. Fourteen cardiovascular complications have been considered. This analysis is conducted using three-way correspondence analysis (CA), which allows for the partitioning and decomposition of Pearson’s three-way chi-squared statistic. The dataset comprises information organized in a data cube, detailing how weight variability among these patients correlates with a cardiovascular complication, age group, and gender. The three-way CA method presented in this paper allows one to assess the statistical significance of the association between these variables and to visualize this association, highlighting the gender gap among these patients. From this analysis, we find that the association between weight variability, age group, and gender varies among different types of cardiovascular complications.

Keywords: cardiovascular complication; Pearson’s three-way chi-squared statistic; partitioning; three-way correspondence analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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