IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v6y2023i3p48-762d1187594.html
   My bibliography  Save this article

Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data

Author

Listed:
  • Veronica Distefano

    (European Centre for Living Technology (ECLT), Ca’ Foscari University of Venice, 30123 Venice, Italy
    Department of Economic Sciences, Università del Salento, 73100 Lecce, Italy)

  • Maria Mannone

    (European Centre for Living Technology (ECLT), Ca’ Foscari University of Venice, 30123 Venice, Italy
    Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Irene Poli

    (European Centre for Living Technology (ECLT), Ca’ Foscari University of Venice, 30123 Venice, Italy)

Abstract

Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the visualization tools provided by category theory, and bridges category-based abstract works and real datasets. We build subgroups deriving clusters of patients at different time points, considering a set of variables characterizing the state of patients. We analyze how specific variables affect the disease progress, and which drug combinations are more effective for each cluster of patients. The retrieved information can foster individualized strategies for DKD treatment.

Suggested Citation

  • Veronica Distefano & Maria Mannone & Irene Poli, 2023. "Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data," Stats, MDPI, vol. 6(3), pages 1-16, July.
  • Handle: RePEc:gam:jstats:v:6:y:2023:i:3:p:48-762:d:1187594
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/6/3/48/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-905X/6/3/48/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. L. Doove & E. Dusseldorp & K. Deun & I. Mechelen, 2014. "A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions," 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. 8(4), pages 403-425, December.
    2. Fuchs, Sebastian & Di Lascio, F. Marta L. & Durante, Fabrizio, 2021. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    3. Nathan Cunningham & Jim E. Griffin & David L. Wild, 2020. "ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification," 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(2), pages 463-484, June.
    4. Seyoung Park & Hao Xu & Hongyu Zhao, 2021. "Integrating Multidimensional Data for Clustering Analysis With Applications to Cancer Patient Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 14-26, March.
    5. Liu, Lili & Lin, Lu, 2019. "Subgroup analysis for heterogeneous additive partially linear models and its application to car sales data," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 239-259.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Habibi Asgarabad, Mojtaba & Vesely, Stepan & Klöckner, Christian Andreas, 2024. "Exploring the Interplay between Structural Factors, Environmental Concern, Personal Norm, and Household Electricity Consumption," OSF Preprints gd5ra, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    2. Cai, Tingting & Li, Jianbo & Zhou, Qin & Yin, Songlou & Zhang, Riquan, 2024. "Subgroup detection based on partially linear additive individualized model with missing data in response," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    3. Mingyang Ren & Qingzhao Zhang & Sanguo Zhang & Tingyan Zhong & Jian Huang & Shuangge Ma, 2022. "Hierarchical cancer heterogeneity analysis based on histopathological imaging features," Biometrics, The International Biometric Society, vol. 78(4), pages 1579-1591, December.
    4. Moritz Berger & Thomas Welchowski & Steffen Schmitz-Valckenberg & Matthias Schmid, 2019. "A classification tree approach for the modeling of competing risks in discrete time," 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. 13(4), pages 965-990, December.
    5. Zhang, Xiaochen & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2022. "Subgroup analysis for high-dimensional functional regression," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    6. Fang, Kuangnan & Chen, Yuanxing & Ma, Shuangge & Zhang, Qingzhao, 2022. "Biclustering analysis of functionals via penalized fusion," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    7. Marjolein Fokkema & Niels Smits & Achim Zeileis & Torsten Hothorn & Henk Kelderman, 2015. "Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees," Working Papers 2015-10, Faculty of Economics and Statistics, Universität Innsbruck.
    8. Wang, Xin & Zhu, Zhengyuan & Zhang, Hao Helen, 2023. "Spatial heterogeneity automatic detection and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    9. Tianjiao Wang & Xiaona Xia, 2023. "The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters," SAGE Open, , vol. 13(2), pages 21582440231, April.
    10. Lingsong Meng & Dorina Avram & George Tseng & Zhiguang Huo, 2022. "Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 352-375, March.
    11. Paolo Onorati & Brunero Liseo, 2022. "Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior," Stats, MDPI, vol. 5(4), pages 1-17, November.
    12. Heidi Seibold & Torsten Hothorn & Achim Zeileis, 2019. "Generalised linear model trees with global additive effects," 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. 13(3), pages 703-725, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:6:y:2023:i:3:p:48-762:d:1187594. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.