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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.

SUBMITTER: Li K

PROVIDER: S-EPMC6681911 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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

Li Kan K Luo Sheng S Yuan Sammy S Mt-Isa Shahrul S

Statistics in medicine 20190415 16

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 th ...[more]

PMID: 30989691

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