Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients

• John L. Mbotwa ,

  Roles Formal analysis, Investigation, Methodology, Writing – original draft

  * E-mail: J.L.Mbotwa@leeds.ac.uk

  Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom, Department of Applied Studies, Malawi University of Science and Technology, Malawi, United Kingdom

  https://orcid.org/0000-0002-9704-8419

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• Marc de Kamps,

  Roles Supervision

  Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, School of Computing, University of Leeds, Leeds, United Kingdom

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• Paul D. Baxter,

  Roles Supervision, Writing – review & editing

  Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom

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• George T. H. Ellison ,

  Roles Writing – review & editing

  ¶‡ These authors are joint senior authors on this work

  Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom, Centre for Data Innovation, University of Central Lancashire, Preston, United Kingdom

  https://orcid.org/0000-0001-8914-6812

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• Mark S. Gilthorpe

  Roles Conceptualization, Writing – review & editing

  ¶‡ These authors are joint senior authors on this work

  Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom, The Alan Turing Institute, London, United Kingdom

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