A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses

Troffaes, Matthias C. M.; Casini, Lorenzo; Landes, Jürgen; Sahlin, Ullrika

Abstract

Meta-analyses are vital for synthesizing evidence in medical research, but conflicts of interest can introduce research bias, undermining the reliability of the synthesized findings. This paper proposes a new robust Bayesian meta-analysis model. The model inflates uncertainty of low-quality studies and incorporates a bias term for studies subject to conflicts of interest. Using a random-effects model and sensitivity analysis with bounded probabilities, the model enables robust adjustments for conflicts of interest in meta-analytic contexts. A case study on antidepressant trials illustrates the potential application of the model.

Citation

Troffaes, M. C. M., Casini, L., Landes, J., & Sahlin, U. (2025, July). A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses. Presented at 14th International Symposium on Imprecise Probabilities: Theories and Applications, Bielefeld, Germany

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 14th International Symposium on Imprecise Probabilities: Theories and Applications
• Start Date: Jul 15, 2025
• End Date: Jul 18, 2025
• Acceptance Date: Apr 18, 2025
• Deposit Date: Feb 7, 2025
• Journal: Proceedings of Machine Learning Research
• Electronic ISSN: 2640-3498
• Publisher: PMLR
• Peer Reviewed: Peer Reviewed
• Series ISSN: 2640-3498
• Keywords: conflict of interest; meta-analysis; sensitivity analysis
• Public URL: https://durham-repository.worktribe.com/output/3471616
• Publisher URL: https://proceedings.mlr.press/