Using multicriteria decision analysis during drug development to predict reimbursement decisions

Area of Science:

• Health Economics
• Decision Science
• Pharmaceutical Policy

Background:

• Pharmaceutical companies design clinical development programs to generate data supporting reimbursement for experimental compounds.
• Estimating the probability of successful reimbursement is crucial for drug development strategy.

Purpose of the Study:

• To present a process for using multicriteria decision analysis (MCDA) to estimate the probability of a positive reimbursement recommendation for new drugs.
• To incorporate drug and environmental attributes into reimbursement probability estimations.

Main Methods:

• Selected decision-makers representative of reimbursement bodies in specific countries.
• Conducted pre-workshop questionnaires to identify key attributes and their importance for reimbursement.
• Held a workshop for attribute finalization, value function development, and hypothetical product profiling.
• Developed a prediction algorithm using logistic regression based on workshop data, illustrated with case studies from the UK, Germany, and Spain.

Main Results:

• Developed country-specific prediction algorithms estimating the probability of positive reimbursement recommendations.
• In the UK, the algorithm predicted based on cost-effectiveness ratios; in Spain, on national and regional levels; in Germany, on clinical benefit determination.
• Post-meeting questionnaires confirmed the high predictive value of the MCDA-developed algorithms.

Conclusions:

• Multicriteria decision analysis (MCDA) can generate prediction algorithms for pharmaceutical companies.
• These algorithms can estimate the likelihood of favorable reimbursement recommendations during clinical development.
• The process allows for evaluating different product profiles and positions within treatment pathways.