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Multilevel cluster-weighted models for the evaluation of hospitals

Author

Listed:
  • Paolo Berta

    (University of Milano-Bicocca)

  • Salvatore Ingrassia

    (University of Catania)

  • Antonio Punzo

    (University of Catania)

  • Giorgio Vittadini

    (University of Milano-Bicocca)

Abstract

In recent years, increasing attention has been directed toward problems inherent to quality control in healthcare services. In particular, it is necessary to measure effectiveness with respect to improving healthcare outcomes of diagnostic procedures or specific treatment episodes. The performance of hospitals is usually evaluated by multilevel models and other methods for risk adjustment. However, these approaches are not suitable for data with large unobserved heterogeneity. A potentially large source of unobserved heterogeneity comes from the variation of the regression coefficients between groups of individuals sharing similar but unobserved characteristics. To overcome such drawbacks, we propose the multilevel cluster-weighted model, a new mixture model approach for handling hierarchical data.

Suggested Citation

  • Paolo Berta & Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini, 2016. "Multilevel cluster-weighted models for the evaluation of hospitals," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 275-292, December.
    • Handle: RePEc:spr:metron:v:74:y:2016:i:3:d:10.1007_s40300-016-0098-3
    DOI: 10.1007/s40300-016-0098-3
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    References listed on IDEAS

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    Cited by:

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    3. Michael P. B. Gallaugher & Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2022. "Multivariate cluster weighted models using skewed distributions," 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. 16(1), pages 93-124, March.
    4. Engel, Christoph, 2020. "Estimating heterogeneous reactions to experimental treatments," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 124-147.
    5. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.
    6. Angelo Mazza & Antonio Punzo, 2020. "Mixtures of multivariate contaminated normal regression models," Statistical Papers, Springer, vol. 61(2), pages 787-822, April.
    7. Alicia Ramírez-Orellana & María del Carmen Valls Martínez & Mayra Soledad Grasso, 2021. "Using Higher-Order Constructs to Estimate Health-Disease Status: The Effect of Health System Performance and Sustainability," Mathematics, MDPI, vol. 9(11), pages 1-23, May.

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