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

Matthias C. M. Troffaes, Lorenzo Casini, Jürgen Landes, Ullrika Sahlin
Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 290:273-284, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v290-troffaes25a, title = {A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses}, author = {Troffaes, Matthias C. M. and Casini, Lorenzo and Landes, J\"urgen and Sahlin, Ullrika}, booktitle = {Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications}, pages = {273--284}, year = {2025}, editor = {Destercke, Sébastien and Erreygers, Alexander and Nendel, Max and Riedel, Frank and Troffaes, Matthias C. M.}, volume = {290}, series = {Proceedings of Machine Learning Research}, month = {15--18 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v290/main/assets/troffaes25a/troffaes25a.pdf}, url = {https://proceedings.mlr.press/v290/troffaes25a.html}, 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.} }
Endnote
%0 Conference Paper %T A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses %A Matthias C. M. Troffaes %A Lorenzo Casini %A Jürgen Landes %A Ullrika Sahlin %B Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications %C Proceedings of Machine Learning Research %D 2025 %E Sébastien Destercke %E Alexander Erreygers %E Max Nendel %E Frank Riedel %E Matthias C. M. Troffaes %F pmlr-v290-troffaes25a %I PMLR %P 273--284 %U https://proceedings.mlr.press/v290/troffaes25a.html %V 290 %X 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.
APA
Troffaes, M.C.M., Casini, L., Landes, J. & Sahlin, U.. (2025). A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses. Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research 290:273-284 Available from https://proceedings.mlr.press/v290/troffaes25a.html.

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