title: A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment.
abstract: 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.
authors: Imperial Clinical Trials Unit, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, London W2 1PG, UK.
authors: Ed
authors: E
authors: Waddingham
authors: Imperial Clinical Trials Unit, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, London W2 1PG, UK.
authors: Shahrul
authors: S
authors: Mt-Isa
authors: Statistical Methodology and Consulting, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland.
authors: Richard
authors: R
authors: Nixon
authors: Imperial Clinical Trials Unit, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, London W2 1PG, UK.
authors: Deborah
authors: D
authors: Ashby
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003661:Decision Support Techniques; D006801:Humans; D009103:Multiple Sclerosis / Q000188:drug therapy; D000069442:Natalizumab / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic; D012008:Recurrence; D018570:Risk Assessment; D035501:Uncertainty
chemical_list: D000069442:Natalizumab
keywords: Bayes; Benefit risk; Decision making; MCDA; Statistics