A case study using the PrOACT‐URL and BRAT frameworks for structured benefit risk assessment. Issue 1 (26th January 2015)

• Record Type: Journal Article
• Authors: Nixon, Richard; Dierig, Christoph; Mt‐Isa, Shahrul; Stöckert, Isabelle; Tong, Thaison; Kuhls, Silvia; Hodgson, Gemma; Pears, John; Waddingham, Ed; Hockley, Kimberley; Thomson, Andrew
• Is Part Of: Biometrical journal. Volume 58:Issue 1(2016:Jan.)
• Page Start: 8
• Page End: 27
• Publication Date: 2015-01-26
• Subjects: Benefit‐risk -- BRAT -- Multicriteria decision analysis -- Net clinical benefit -- PrOACT‐URL -- Regulatory decision making; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201300248
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract : While benefit‐risk assessment is a key component of the drug development and maintenance process, it is often described in a narrative. In contrast, structured benefit‐risk assessment builds on established ideas from decision analysis and comprises a qualitative framework and quantitative methodology. We compare two such frameworks, applying multi‐criteria decision‐analysis (MCDA) within the PrOACT‐URL framework and weighted net clinical benefit (wNCB), within the BRAT framework. These are applied to a case study of natalizumab for the treatment of relapsing remitting multiple sclerosis. We focus on the practical considerations of applying these methods and give recommendations for visual presentation of results. In the case study, we found structured benefit‐risk analysis to be a useful tool for structuring, quantifying, and communicating the relative benefit and safety profiles of drugs in a transparent, rational and consistent way. The two frameworks were similar. MCDA is a generic and flexible methodology that can be used to perform a structured benefit‐risk in any common context. wNCB is a special case of MCDA and is shown to be equivalent to an extension of the number needed to treat (NNT) principle. It is simpler to apply and understand than MCDA and can be applied when all outcomes are measured on a binary scale.