A Bayesian approach for individual-level drug benefit-risk assessment.
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.
Duke Scholars
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Related Subject Headings
- Treatment Outcome
- Stochastic Processes
- Statistics & Probability
- Risk Assessment
- Pharmaceutical Preparations
- Monte Carlo Method
- Markov Chains
- Humans
- Decision Support Techniques
- Computer Simulation
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Treatment Outcome
- Stochastic Processes
- Statistics & Probability
- Risk Assessment
- Pharmaceutical Preparations
- Monte Carlo Method
- Markov Chains
- Humans
- Decision Support Techniques
- Computer Simulation