Predictors of High Healthcare Cost Among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a US Payer Perspective.

Journal: Applied health economics and health policy
PMID: 39002043

Abstract

BACKGROUND: High healthcare costs could arise from unmet needs. This study used random forest (RF) and regression methods to identify predictors of high costs from a US payer perspective in patients newly diagnosed with generalized myasthenia gravis (gMG).

Authors

  • Maryia Zhdanava
    Analysis Group, Inc., Montréal, QC, Canada. Masha.Zhdanava@analysisgroup.com.
  • Jacqueline Pesa
    Janssen Scientific Affairs, LLC, a Johnson & Johnson company, Titusville, NJ, USA.
  • Porpong Boonmak
    Analysis Group, Inc., Montréal, QC, Canada.
  • Samuel Schwartzbein
    Analysis Group, Inc., Montréal, QC, Canada.
  • Qian Cai
    Department of Otolaryngology-Head and Neck, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Dominic Pilon
    Analysis Group, Inc., Montréal, QC, Canada.
  • Zia Choudhry
    Janssen Scientific Affairs, LLC, a Johnson & Johnson company, Titusville, NJ, USA.
  • Marie-Hélène Lafeuille
    Analysis Group, Inc., Montréal, QC, Canada.
  • Patrick Lefebvre
    Analysis Group, Inc., Montréal, QC, Canada.
  • Nizar Souayah
    Department of Neurology and Neurosciences, Rutgers-New Jersey Medical School, Newark, NJ, USA.

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