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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

Author

Listed:
  • 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
    Centre for Multi-Dimensional Data Visualisation (MuViSU), Stellenbosch University, Stellenbosch 7602, South Africa)

  • 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)

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.

Suggested Citation

  • Rosaria Lombardo & Eric J. Beh & Francesco Prattichizzo & Giuseppe Lucisano & Antonio Nicolucci & Björn Eliasson & Hanne Krage Carlsen & Rosalba La Grotta & Valeria Pellegrini & Antonio Ceriello, 2024. "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," Mathematics, MDPI, vol. 12(14), pages 1-13, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2186-:d:1434003
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    References listed on IDEAS

    as
    1. Rosaria Lombardo & Yoshio Takane & Eric J. Beh, 2020. "Familywise decompositions of Pearson’s chi-square statistic in the analysis of contingency tables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 629-649, September.
    2. Sébastien Loisel & Yoshio Takane, 2016. "Partitions of Pearson’s Chi-square statistic for frequency tables: a comprehensive account," Computational Statistics, Springer, vol. 31(4), pages 1429-1452, December.
    3. Eric J. Beh & Rosaria Lombardo, 2015. "Confidence Regions and Approximate p-values for Classical and Non Symmetric Correspondence Analysis," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(1), pages 95-114, January.
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