A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment

Quantitative decision models such as multiple criteria decision analysis (MCDA) can be used in benefit‐risk assessment to formalize trade‐offs between benefits and risks, providing transparency to the assessment process. There is however no well‐established method for propagating uncertainty of trea...

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Bibliographic Details
Published in:Biometrical journal 2016-01, Vol.58 (1), p.28-42
Main Authors: Waddingham, Ed, Mt-Isa, Shahrul, Nixon, Richard, Ashby, Deborah
Format: Article
Language:English
Subjects:
Bayes
Bayes Theorem
Bayesian analysis
Benefit risk
Biometry - methods
Decision making
Decision Support Techniques
Humans
Markov analysis
Markov chains
MCDA
Monte Carlo simulation
Multiple Sclerosis - drug therapy
Natalizumab - therapeutic use
Randomized Controlled Trials as Topic
Recurrence
Risk Assessment
Sensitivity analysis
Statistical methods
Statistics
Uncertainty
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Summary:Quantitative decision models such as multiple criteria decision analysis (MCDA) can be used in benefit‐risk assessment to formalize trade‐offs between benefits and risks, providing transparency to the assessment process. There is however no well‐established method for propagating uncertainty of treatment effects data through such models to provide a sense of the variability of the benefit‐risk balance. Here, we present a Bayesian statistical method that directly models the outcomes observed in randomized placebo‐controlled trials and uses this to infer indirect comparisons between competing active treatments. The resulting treatment effects estimates are suitable for use within the MCDA setting, and it is possible to derive the distribution of the overall benefit‐risk balance through Markov Chain Monte Carlo simulation. The method is illustrated using a case study of natalizumab for relapsing‐remitting multiple sclerosis.
ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.201300254