OBJECTIVES: Value-based contracts (VBCs) are innovative payment models that tie reimbursement to patient outcomes. VBCs are particularly relevant for cell and gene therapies (CGTs) with high upfront cost and uncertainties in real-world performance. As VBCs can be complex and costly to implement, at times flat-rate discounts may be preferable because they are easy to implement with predictable costs. We conducted a probabilistic tipping point analysis comparing reimbursement schemes to optimize VBC negotiations.

METHODS: We conducted a simulation-based tipping point analysis case study based on a real-world VBC for a sickle cell disease CGT. We calculated the flat-rate discount that was equivalent to the VBC under competing CGT performance expectations that reflected clinical trial results and expert beliefs about real-world treatment effectiveness. Based on input from CGT and VBC leaders, we assumed: 5 eligible patients per year; treatment cost of $3,200,000 per patient; 87.1% (n=34/39) success rate in the clinical trial; 75% payer expected success rate; and 50% rebate per patient for vascular-occlusion related hospitalization.

RESULTS: Without accounting for any specific costs for VBCs, we calculated the equivalent flat discount rate with VBCs. Provided treatment performance matched clinical trials, the equivalent flat discount to a 50% rebate per failure would be 7.3% (95% credible interval [95%CrI]: 2.9-13.5%). In contrast, the equivalent flat discount under payer expectations would be 13.4% (95% CrI: 7.4-20.4%). Accounting for costs specific to VBCs, such as patient outcome tracking, would lower the flat discount rate further.

CONCLUSIONS: While VBCs offer opportunities to improve access to CGTs, implementing VBCs can be complex. VBC specific costs such as patient outcome tracking should be accounted for in negotiations as flat-rate discounting mitigates this need. A probabilistic tipping point analysis can provide novel insights and optimize reimbursement scheme negotiations given the clinical context, existing evidence and uncertainty about real world effectiveness.