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- W1678633222 abstract "Regulatory agencies show a growing interest in quantitative models for risk-benefit assessments to increase decision transparency. Regulators increasingly incorporate patients' view on benefit-risk tradeoffs but little is known on how to integrate elicited preferences into the quantitative models. There is little knowledge on how to integrate these preferences with clinical performance data and how to use knowledge about the uncertainty surrounding both types of parameters (preference and performance). The objective of this study was to demonstrate how patient preferences can be integrated in a Bayesian framework for quantitative benefit-risk assessment. An MCDA model was developed that integrates clinical trial data, elicited patient preferences and uncertainty surrounding these estimates. Stochastic characteristics of preference and drug performance parameters can be approximated from stated preference studies and performance data from systematic reviews or RCT's. Risk and benefit scores of drugs are then simulated with Monte Carlo methods using approximated distributions. The acceptability (runs where weighted benefits > weighted risks) is calculated. A ‘benefit-risk factsheet’ with acceptability graphs is provided, to facilitate decision makers in their appraisal. The model was applied to an anti-depressants case. We included two benefit and one risk criteria. Preference data was derived from an expert panel and the performance data (pooled odds ratio's compared to placebo) were derived from a systematic review. The simulations show all drugs have high (≈1) acceptabilities. The problem is more sensitive to performance information than to preference information and most sensitive to the adverse events performance criterion. Using this MCDA model it is possible to include patient preference in a quantitative risk-benefit assessment model. The model allows integration of stochastic uncertainty concerning preference and performance. It demonstrates that comprehensive presentation of data is possible. We are currently working on applying the model to a case on advanced renal cell carcinoma." @default.
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- W1678633222 date "2012-11-01" @default.
- W1678633222 modified "2023-09-28" @default.
- W1678633222 title "PRM79 Integrating Patient Preferences and Clinical Trial Data in a Bayesian Model for Quantitative Risk-Benefit Assessment" @default.
- W1678633222 doi "https://doi.org/10.1016/j.jval.2012.08.1542" @default.
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