A case study using the PrOACT‐URL and BRAT frameworks for structured benefit risk assessment

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.

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

“…These results in some minor differences in our relapse figures from those reported in Nixon et al. ().…”

“…This paper uses the partial value functions and weights selected by Nixon et al. () for the natalizumab case study. The case study aimed to assess the benefit‐risk balance from a regulatory perspective, with the regulator incorporating the views of patients.…”

“…Further information on the case study has been given by Nixon et al. (); a summary of the key data is provided in Table .…”

“…In the initial case study, Nixon et al. () used MCDA as a quantitative tool in combination with the PrOACT‐URL decision‐making framework (Hammond et al., ). This paper aims to extend this work by considering the impact of data uncertainty using a probabilistic model.…”

Leadership in Statistics: Increasing Our Value and Visibility

“…The TPP provides a range of outcomes against which the cumulative clinical efficacy and safety data are compared at each milestone between development phases, such as the transition from phase 2 to phase 3. The posterior distribution of the treatment effect for each endpoint based on the accumulated data is used to calculate the likelihood of exceeding either of the targets specified in the TPP (Nixon et al 2016). This quantifies the cumulative evidence relative to the TPP objectives and accounts for the uncertainty depending on the amount of available data.…”