Feb 1, 2018, 00:00 AM

10.1016/j.jval.2017.04.011

https://www.valueinhealthjournal.com/article/S1098-3015(17)30211-5/fulltext

When proven effective, decision making regarding reimbursement of new health technology typically involves ethical, social, legal, and health economic aspects and constraints. Nevertheless, when applying standard value of information (VOI) analysis, the value of collecting additional evidence is typically estimated assuming that only cost-effectiveness outcomes guide such decisions.

To illustrate how decision makers’ constraints can be incorporated into VOI analyses and how these may influence VOI outcomes.

A simulation study was performed to estimate the cost-effectiveness of a new hypothetical technology compared with usual care. Constraints were defined for the new technology on 1) the maximum acceptable rate of complications and 2) the maximum acceptable additional budget. The expected value of perfect information (EVPI) for the new technology was estimated in various scenarios, both with and without incorporating these constraints.

For a willingness-to-pay threshold of €20,000 per quality-adjusted life-year, the probability that the new technology was cost-effective equaled 57%, with an EVPI of €1868 per patient. Applying the complication rate constraint reduced the EVPI to €1137. Similarly, the EVPI reduced to €770 when applying the budget constraint. Applying both constraints simultaneously further reduced the EVPI to €318.

When decision makers explicitly apply additional constraints, beyond a willingness-to-pay threshold, to reimbursement decisions, these constraints can and should be incorporated into VOI analysis as well, because they may influence VOI outcomes. This requires continuous interaction between VOI analysts and decision makers and is expected to improve both the relevance and the acceptance of VOI outcomes.

https://www.valueinhealthjournal.com/action/showCitFormats?pii=S1098-3015(17)30211-5&doi=10.1016/j.jval.2017.04.011

- decision making
- multiple constraints
- reimbursement
- research prioritization
- value of information