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A semiparametric Bayesian joint model for multiple mixed-type outcomes: an application to acute myocardial infarction

Author

Listed:
  • Alessandra Guglielmi

    (Politecnico di Milano)

  • Francesca Ieva

    (Università degli Studi di Milano)

  • Anna Maria Paganoni

    (Politecnico di Milano)

  • Fernardo A. Quintana

    (Pontificia Universidad Católica de Chile)

Abstract

We propose a Bayesian semiparametric regression model to represent mixed-type multiple outcomes concerning patients affected by Acute Myocardial Infarction. Our approach is motivated by data coming from the ST-Elevation Myocardial Infarction (STEMI) Archive, a multi-center observational prospective clinical study planned as part of the Strategic Program of Lombardy, Italy. We specifically consider a joint model for a variable measuring treatment time and in-hospital and 60-day survival indicators. One of our main motivations is to understand how the various hospitals differ in terms of the variety of information collected as part of the study. To do so we postulate a semiparametric random effects model that incorporates dependence on a location indicator that is used to explicitly differentiate among hospitals in or outside the city of Milano. The model is based on the two parameter Poisson-Dirichlet prior, also known as the Pitman-Yor process prior. We discuss the resulting posterior inference, including sensitivity analysis, and a comparison with the particular sub-model arising when a Dirichlet process prior is assumed.

Suggested Citation

  • Alessandra Guglielmi & Francesca Ieva & Anna Maria Paganoni & Fernardo A. Quintana, 2018. "A semiparametric Bayesian joint model for multiple mixed-type outcomes: an application to acute myocardial infarction," 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. 12(2), pages 399-423, June.
    • Handle: RePEc:spr:advdac:v:12:y:2018:i:2:d:10.1007_s11634-016-0273-7
    DOI: 10.1007/s11634-016-0273-7
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    References listed on IDEAS

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    1. De Iorio, Maria & Muller, Peter & Rosner, Gary L. & MacEachern, Steven N., 2004. "An ANOVA Model for Dependent Random Measures," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 205-215, January.
    2. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
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    Cited by:

    1. Kang, Xiaoning & Kang, Lulu & Chen, Wei & Deng, Xinwei, 2022. "A generative approach to modeling data with quantitative and qualitative responses," Journal of Multivariate Analysis, Elsevier, vol. 190(C).

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