Presentation (CTI)

OBJECTIVES: The U.S. Second Panel on Cost-Effectiveness and the ISPOR Drug Cost Task Force recommend that prices in economic evaluations reflect stakeholder “opportunity costs”. In practice, this is frequently violated: (1) price metrics often fail to reflect the relevant opportunity cost (e.g., using list price—or sometimes manufacturer net price—in societal and healthcare-sector analyses); and (2) prices are routinely assumed static over product life-cycles, despite changes before and after loss of exclusivity (LOE). Approaches are emerging to address both limitations. We aim to provide clarity on how different U.S. price metrics represent different analytic perspectives and review and classify approaches for incorporating price dynamics.
METHODS: We conducted a targeted search of published studies and gray literature on drug price metrics and dynamics, complemented by citation searches. Price metrics were mapped to key stakeholder perspectives along the supply and payment chains for both retail and specialty products, and dynamic pricing approaches were systematically classified. Data requirements are highlighted, as are implications for economic modelling.
RESULTS: The resulting map of drug price metrics shows which best align with the different transactions along the supply and payment chain and indicates if they need to be estimated using data sources such as healthcare claims, financial reporting, and statutory benchmarks. In addition, we note whether and how the metrics diverge from true opportunity cost. Five distinct approaches to modeling drug price dynamics were identified—one-time reduction at LOE, price erosion curves, analog-based forecasts, cost-based pricing floors, and structural competition models. Data needs and implications for value assessment and budget impact are presented for each approach.
CONCLUSIONS: Economic evaluations can only be informative for resource allocation decisions if they sufficiently reflect actual opportunity costs. This work identifies important sources of bias from mis-specified pricing assumptions in the U.S. and thereby facilitates decision-relevant economic evaluations.