Purpose: To develop a general, unified theoretical framework which combines assessments of: i) cost-effectiveness, ii) the need for different types of evidence, ii) sources of uncertainty which cannot be resolved by research but only over time, and iv) investment and future reversal costs, within which the range of possible decisions (‘only in research’ (OIR), ‘approval with evidence development’ (AED), Approve or Reject) can be considered.
Method: Decisions are commonly made when there is uncertainty surrounding cost-effectiveness and where further evidence may be valuable. However, approval can have an impact on the prospects of acquiring particular types of evidence and may require investment or impose future reversal costs. There may also be other sources of uncertainty which can only be resolved over time. Each of these considerations interact, making the implications for reimbursement and research decisions complex and sometimes unexpected. Through a sequence of numerical examples, generalised through notation, each of these previously but separately considered areas are unified. A single numerical example, comparing two technologies over 3 time periods is built upon in stages (with general notation) providing an intuitive demonstration of the potential impact of each additional consideration; explaining the sometimes complex interactions. The resulting framework is used to construct an algorithm for policy choice, identifying the appropriate sequence of assessments which lead to different types of reimbursement and research decisions.
Result: The framework indicates that policies such as OIR, AED, and Reject have much wider application than commonly observed. It demonstrates there are very many different ‘types’ of OIR, AED, and Reject decisions each reflecting a different combination of assessment. Understanding how each combination leads to particular policy choice is critical when assessing how decisions might be revised through changes to the technology (an effective price reduction or additional evidence) or the policy environment (predictability of research and more effective implementation of decisions).
Conclusion: Previous research has addressed the assessments required to judge cost-effectiveness, the value of further evidence and the impact of investment costs, but none has provided a unifying general framework for this series of interlinked considerations. The framework and associated algorithm provides clear guidance for the sequence of assessments required and how they lead to a range of possible reimbursement and research decisions.
See more of: The 32nd Annual Meeting of the Society for Medical Decision Making