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A Bayesian approach for individual-level drug benefit-risk assessment.

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Date
2019-07
Authors
Li, Kan
Luo, Sheng
Yuan, Sammy
Mt-Isa, Shahrul
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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 article
Subject
MCMC
SMAA
latent trait model
patient centered approach
Permalink
https://hdl.handle.net/10161/19136
Published Version (Please cite this version)
10.1002/sim.8166
Publication 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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Scholars@Duke

Luo

Sheng Luo

Professor of Biostatistics & Bioinformatics
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