Predicting Patient-Level 3-Level Version of EQ-5D Index Scores From a Large International Database Using Machine Learning and Regression Methods.

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
PMID: 35300933

Abstract

OBJECTIVES: This study aimed to evaluate the performance of machine learning and regression methods in the prediction of 3-level version of EQ-5D (EQ-5D-3L) index scores from a large diverse data set.

Authors

  • Zsombor Zrubka
    Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary; Corvinus Institue for Advanced Studies, Corvinus University of Budapest, Budapest, Hungary. Electronic address: zrubka.zsombor@uni-obuda.hu.
  • István Csabai
    Department of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary.
  • Zoltán Hermann
    Institute of Economics, Centre for Economic and Regional Studies, Budapest, Hungary; Institute of Economics, Corvinus University of Budapest, Budapest, Hungary.
  • Dominik Golicki
    Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland.
  • Valentina Prevolnik-Rupel
    Institute for Economic Research, Ljubljana, Slovenia.
  • Marko Ogorevc
    Institute for Economic Research, Ljubljana, Slovenia.
  • László Gulácsi
    Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary; Corvinus Institue for Advanced Studies, Corvinus University of Budapest, Budapest, Hungary.
  • Márta Péntek
    Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary.

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