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A Bayesian approach for individual-level drug benefit-risk assessment.
Abstract
In existing benefit-risk assessment (BRA) methods, benefit and risk criteria are usually
identified and defined separately based on aggregated clinical data and therefore
ignore the individual-level differences as well as the association among the criteria.
We proposed a Bayesian multicriteria decision-making method for BRA of drugs using
individual-level data. We used a multidimensional latent trait model to account for
the heterogeneity of treatment effects with latent variables introducing the dependencies
among outcomes. We then applied the stochastic multicriteria acceptability analysis
approach for BRA incorporating imprecise and heterogeneous patient preference information.
We adopted an efficient Markov chain Monte Carlo algorithm when implementing the proposed
method. We applied our method to a case study to illustrate how individual-level benefit-risk
profiles could inform decision-making.
Type
Journal articlePermalink
https://hdl.handle.net/10161/19136Published Version (Please cite this version)
10.1002/sim.8166Publication Info
Li, Kan; Luo, Sheng; Yuan, Sammy; & Mt-Isa, Shahrul (2019). A Bayesian approach for individual-level drug benefit-risk assessment. Statistics in medicine, 38(16). pp. 3040-3052. 10.1002/sim.8166. Retrieved from https://hdl.handle.net/10161/19136.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Sheng Luo
Professor of Biostatistics & Bioinformatics

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