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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Li, K; Luo, S; Yuan, S; Mt-Isa, S

Published Date

  • July 20, 2019

Published In

Volume / Issue

  • 38 / 16

Start / End Page

  • 3040 - 3052

PubMed ID

  • 30989691

Pubmed Central ID

  • 30989691

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

Digital Object Identifier (DOI)

  • 10.1002/sim.8166

Language

  • eng

Conference Location

  • England