title: Two approaches to incorporate clinical data uncertainty into multiple criteria decision analysis for benefit-risk assessment of medicinal products.
abstract: BACKGROUND
  The Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions (PrOACT-URL) framework and multiple criteria decision analysis (MCDA) have been recommended by the European Medicines Agency for structured benefit-risk assessment of medicinal products undergoing regulatory review.
  
  
  OBJECTIVE
  The objective of this article was to provide solutions to incorporate the uncertainty from clinical data into the MCDA model when evaluating the overall benefit-risk profiles among different treatment options.
  
  
  METHODS
  Two statistical approaches, the δ-method approach and the Monte-Carlo approach, were proposed to construct the confidence interval of the overall benefit-risk score from the MCDA model as well as other probabilistic measures for comparing the benefit-risk profiles between treatment options. Both approaches can incorporate the correlation structure between clinical parameters (criteria) in the MCDA model and are straightforward to implement.
  
  
  RESULTS
  The two proposed approaches were applied to a case study to evaluate the benefit-risk profile of an add-on therapy for rheumatoid arthritis (drug X) relative to placebo. It demonstrated a straightforward way to quantify the impact of the uncertainty from clinical data to the benefit-risk assessment and enabled statistical inference on evaluating the overall benefit-risk profiles among different treatment options.
  
  
  CONCLUSIONS
  The δ-method approach provides a closed form to quantify the variability of the overall benefit-risk score in the MCDA model, whereas the Monte-Carlo approach is more computationally intensive but can yield its true sampling distribution for statistical inference. The obtained confidence intervals and other probabilistic measures from the two approaches enhance the benefit-risk decision making of medicinal products.
authors.affiliation: Data and Statistical Sciences, AbbVie, Inc., North Chicago, IL, USA. Electronic address: shihua.wen@abbvie.com.
authors.forename: Shihua
authors.initials: S
authors.lastname: Wen
authors.affiliation: Data and Statistical Sciences, AbbVie, Inc., North Chicago, IL, USA.
authors.forename: Lanju
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Data and Statistical Sciences, AbbVie, Inc., North Chicago, IL, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Yang
mesh_terms: D018501:Antirheumatic Agents / Q000009:adverse effects / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000188:drug therapy*; D016001:Confidence Intervals; D003657:Decision Making; D003661:Decision Support Techniques; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D018570:Risk Assessment / Q000379:methods*; D035501:Uncertainty
chemical_list: D018501:Antirheumatic Agents
keywords: multiple criteria decision analysis (MCDA); probabilistic sensitivity analysis; regulatory decision making; structured benefit-risk assessment of medicinal products