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

• 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
• ISSN: 0323-3847; 1521-4036
• DOI: 10.1002/bimj.201300254

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.