OBJECTIVES: Performance-based payment arrangements for innovative drugs seek to allocate fi nancial risk of interventions between manufacturers and payers. Given operational challenges in monitoring real-world outcomes, effective risk sharing mechanisms may require prediction of incremental survival/quality-adjusted life years (QALYs) conditional on surrogate markers, for example, complete remission (CR) or partial remission (PR). In accepting a risk-sharing arrangement that reimburses only for remitters, payers should minimize the “false-positive” (FP) risk: early remitters who subsequently have limited QALYs. Manufacturers should minimize the “falsenegative” (FN) risk: early non-remitters who subsequently have prolonged QALYs. A Bayesian decision framework can be used to choose among multiple likelihood (predictive) priors to optimize the posterior economic risk trade-off for payers and manufacturers. METHODS: A Bayesian decision-analytic, hypothetical data-based, cost-effectiveness model was developed. Prior probabilities and QALYs were assigned for 6-, 3-, and 1-month survival, as were treatment costs. A plausible prior likelihood (predictive) structure represented the (ROC) relationship between the sensitivity and specifi city of CR/PR in predicting survival. Expected (posterior) probabilities of survival, conditional on CR/PR, were generated. At a threshold of $50,000/QALY, the cost-effectiveness of the intervention, conditional on achieving CR/PR, and an optimal sensitivity-specifi city trade-off was derived. RESULTS: At a hypothetical treatment cost of $5,000/month for a 4-month cycle, a minimal FP of 13% (maximum specifi city of 87%) and a minimal FN of 33% (maximum sensitivity of 67%) emerged as necessary to be accepted by payers and manufacturers respectively to ensure viable risksharing. At higher sensitivity, payer risk did not meet the reimbursement threshold, while at higher specifi city, manufacturers would assume excessive fi nancial risk. Other illustrations will be discussed. CONCLUSIONS: Manufacturers should propose evidence-based payment arrangements that utilize clinical trial data to develop economic implications of being at various points on the ROC curve in order to optimize the trade-offs between payer and manufacturer incentives.
Mallick, R., & Hollenbeak, C. (2009). RA3 A BAYESIAN DECISION-ANALYTIC ECONOMIC MODEL TO OPTIMIZE ALLOCATION OF RISK IN PAY-FOR-PERFORMANCE PAYMENT ARRANGEMENTS. Value in Health, 12(3), A18. https://doi.org/10.1016/s1098-3015(10)73148-x