Bayesian Sparse Modeling to Identify High-Risk Subgroups in Meta-Analysis of Safety Data

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Author(s): Qi, Xinyue (ORCID Qi, Xinyue (ORCID 0000-0002-3954-0974); Zhou, Shouhao (ORCID Zhou, Shouhao (ORCID 0000-0002-8124-5047); Wang, Yucai (ORCID Wang, Yucai (ORCID 0000-0002-1576-8341); Peterson, Christine (ORCID Peterson, Christine (ORCID 0000-0003-3316-0468)
  • Language:
    English
  • Source:
    Research Synthesis Methods. Nov 2022 13(6):807-820.
  • Publication Date:
    2022
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: [email protected]; Web site: https://www.wiley.com/en-us
    • Peer Reviewed:
      Y
    • Source:
      14
    • Sponsoring Agency:
      National Cancer Institute (NCI) (DHHS/NIH)
    • Contract Number:
      P30CA016672
    • Subject Terms:
      Bayesian Statistics; Identification; Meta Analysis; Data Analysis; Safety; Medicine; Intervention; At Risk Persons; Hierarchical Linear Modeling; Cancer; Therapy
    • Accession Number:
      10.1002/jrsm.1597
    • ISSN:
      1759-2879
      1759-2887
    • Abstract:
      Meta-analysis allows researchers to combine evidence from multiple studies, making it a powerful tool for synthesizing information on the safety profiles of new medical interventions. There is a critical need to identify subgroups at high risk of experiencing treatment-related toxicities. However, this remains quite challenging from a statistical perspective as there are a variety of clinical risk factors that may be relevant for different types of adverse events, and adverse events of interest may be rare or incompletely reported. We frame this challenge as a variable selection problem and propose a Bayesian hierarchical model which incorporates a horseshoe prior on the interaction terms to identify high-risk groups. Our proposed model is motivated by a meta-analysis of adverse events in cancer immunotherapy, and our results uncover key factors driving the risk of specific types of treatment-related adverse events.
    • Abstract:
      As Provided
    • Publication Date:
      2022
    • Accession Number:
      EJ1354901