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ERIC Number: EJ1354901
Record Type: Journal
Publication Date: 2022-Nov
Pages: 14
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1759-2879
EISSN: EISSN-1759-2887
Available Date: N/A
Bayesian Sparse Modeling to Identify High-Risk Subgroups in Meta-Analysis of Safety Data
Research Synthesis Methods, v13 n6 p807-820 Nov 2022
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.
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www-wiley-com.bibliotheek.ehb.be/en-us
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Cancer Institute (NCI) (DHHS/NIH)
Authoring Institution: N/A
Grant or Contract Numbers: P30CA016672
Author Affiliations: N/A