Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis.
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Benefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process.
Multi-criteria decision analysis
Periodic benefit-risk evaluation report
Stochastic multi-criteria acceptability analysis
Structured benefit-risk assessment
Published Version (Please cite this version)10.1016/j.cct.2018.02.016
Publication InfoLuo, Sheng; Li, Kan; Yuan, Shuai Sammy; Wang, William; Wan, Shuyan Sabrina; Ceesay, Paulette; ... Mt-Isa, Shahrul (2018). Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis. Contemporary clinical trials, 67. pp. 100-108. 10.1016/j.cct.2018.02.016. Retrieved from https://hdl.handle.net/10161/19151.
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