A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment
File(s)
Accepted version
Author(s)
Waddingham, E
Mt-Isa, S
Nixon, R
Ashby, D
Type
Journal Article
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.
Date Issued
2016-01-04
Date Acceptance
2014-10-23
Citation
Biometrical Journal, 2016, 58 (1), pp.28-42
ISSN
1521-4036
Publisher
Wiley-VCH Verlag
Start Page
28
End Page
42
Journal / Book Title
Biometrical Journal
Volume
58
Issue
1
Copyright Statement
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. This is the accepted version of the following article: Waddingham, E., Mt-Isa, S., Nixon, R. and Ashby, D. (2016), A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment. Biom. J., 58: 28–42, which has been published in final form at http://dx,doi.org/10.1002/bimj.201300254
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Mathematical & Computational Biology
Statistics & Probability
Mathematics
Bayes
Benefit risk
Decision making
MCDA
Statistics
RELAPSING MULTIPLE-SCLEROSIS
PLACEBO-CONTROLLED TRIAL
PRIOR DISTRIBUTIONS
METAANALYSIS
NATALIZUMAB
0104 Statistics
Publication Status
Published
Date Publish Online
2015-01-28