PNS225 A UNIFIED ANALYSIS AND MODELING FRAMEWORK FOR THE DESIGN OF OUTCOMES-BASED RISK-SHARING AGREEMENTS

Abstract

Despite the interest in outcomes-based risk-sharing agreements (OBRSAs), there is a lack of systematic methodology to design and assess them. Using the decision analytic framework, an exploratory analysis was conducted to estimate the cost and health benefits of implementing an OBRSA. A contract simulation model was developed, applying the decision analytic framework with four modules: patient population, costs, outcomes, and contract terms. The model inputs included drug costs, healthcare resource utilization costs, and disease progression. Two types of real-world evidence inputs informed the contract simulation model: actuarial analyses and longitudinal, retrospective analyses. The model outputs included net revenue and costs from manufacturer’s and payer’s perspectives, respectively. We applied the simulation model to a hypothetical, first-in-class, disease-modifying therapy for prodromal Alzheimer’s disease (AD). Proportion of patients progressing to AD was the primary outcome for the OBRSA contract. The analyses compared OBRSAs with traditional volume-based contracts. In the base-case, the OBRSA assumed 10% increased drug utilization from better tier placement over the traditional contract. An additional 5% OBRSA rebate was included if primary outcome was not met. Manufacturer bore OBRSA implementation costs. Initial base-case scenarios showed OBRSA as less favorable to both manufacturer and payer, compared to a traditional contract. Scenarios with increased diagnostic testing and drug utilization found OBRSAs to be more favorable than traditional contracts for the manufacturer (no change from base-case to payer). From a payer perspective, although none of the scenarios showed OBRSAs as more favorable than traditional contracts, payer risk was mitigated when clinical outcomes were not met. For OBRSAs to be successful, the payer and manufacturer must enter into partnership with clear understanding of the benefits and risks of such arrangements. This analysis using the modeling framework reduced uncertainty through the evaluation of scenarios designed to identify win-win arrangements.