Why bring cost into optimization phase decision-making? Part 1: Recent findings in Decision Analysis for Intervention Value Efficiency (DAIVE).
Jillian C. Strayhorn

Decision Analysis for Intervention Value Efficiency (DAIVE) is a multi-criteria decision-making framework that supports identification of an optimized intervention, based on data from a factorial optimization trial. This presentation offers a brief introduction to DAIVE and to the concept of value efficiency and debuts some recent findings that help to explain why DAIVE’s approach outperforms alternatives for decision-making in the optimization phase of MOST. We then use an empirical example from digital mental health to demonstrate how incorporating the consideration of cost (broadly defined, and not limited to cost in dollars) can facilitate identification of an optimized intervention—even when cost is not seen as central to the optimization objective.
‍
Why bring cost into optimization phase decision-making? Part 2: Distinguishing cost-effectiveness analysis and value efficiency.
David J. Vanness

Traditional cost-effectiveness analysis and value efficiency both offer approaches to “strategically balancing” effectiveness and cost, and the two approaches share conceptual roots in Pareto efficiency and dominance. This presentation addresses this shared foundation, before identifying key differences between the approaches. We consider cases in which cost is more limiting, and thus more central to the optimization objective, and explain why we see value efficiency as particularly well-suited to the decision-making that takes place in the optimization phase of MOST. We offer key takeaways for those interested in incorporating economic considerations when identifying an optimized intervention.