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A Comprehensive Approach to Benefit–Risk Assessment in Drug Development
Author(s) -
Sarac Sinan B.,
Rasmussen Christian H.,
Rasmussen Morten A.,
Hallgreen Christine E.,
Søeborg Tue,
ColdingJørgensen Morten,
Christensen Per K.,
Thirstrup Steffen,
Mosekilde Erik
Publication year - 2012
Publication title -
basic and clinical pharmacology and toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.805
H-Index - 90
eISSN - 1742-7843
pISSN - 1742-7835
DOI - 10.1111/j.1742-7843.2012.00871.x
Subject(s) - weighting , risk analysis (engineering) , context (archaeology) , computer science , risk assessment , identification (biology) , clinical trial , data mining , management science , medicine , engineering , paleontology , botany , computer security , radiology , pathology , biology
Major regulatory agencies, for example, FDA and EMA , have started to request comprehensive benefit–risk analyses of pharmaceutical products prior to approval or labelling expansion. The purpose of this study is to develop a generally applicable and reliable data‐driven benefit–risk assessment method, where two or more drugs/doses can be compared. Our aim is to formulate an approach that is simple to apply, allows direct comparison of different types of risks and benefits, and is tailored for application in different disease areas both during clinical development and in the marketing approval phase. The proposed benefit–risk assessment method involves eight successive steps: (1) establishment of the decision context, (2) identification of benefit and risk criteria, (3) weighting, (4) scoring, (5) evaluation of uncertainty, (6) calculation of weighted scores, (7) visualization, and (8) discussion and formulation of an overall conclusion. To reduce the impact of subjective judgements, scores are assigned to each criterion on the basis of objective information (data) wherever possible. The proposed benefit–risk evaluation approach offers comprehensive, data‐driven assessments that can facilitate decision processes. It employs descriptive statistical methods to highlight the clinically significant differences between drugs in clinical trials. The approach can be used in single as well as in multiple trials and provides clear diagrams as the basis for presentation and discussion of the results.