PURPOSE

: To illustrate approaches that leverage real-world data (RWD) in order to extend the survival curve in cost-effectiveness analyses (CEA) to a time horizon that meets the needs of decision makers performing health technology assessment.

DESCRIPTION

: Decision makers often require an assessment of the cost-effectiveness of novel interventions over a time horizon that exceeds the duration of randomized controlled trials (RCTs). In such cases where overall survival (OS) data from RCTs are too immature to meet the needs of HTA evaluators, standard methods extrapolate the OS curve using parametric models that rely on assumptions about the tail end of curve.

Alternative approaches to extrapolation involve incorporating data from external sources such as registries, claims, or electronic health records into the model to inform OS beyond the end of RCTs. In this workshop, we will present and illustrate the following approaches: 1) using RWD to validate extrapolated survival assumptions from a parametric model; and 2) a two-phase modeling approach combining extrapolated survival using RWD with survival from a trial population.

The workshop agenda consists of: 1) Dr. Malone presenting an overview of the challenges associated with components of CEA that rely on extrapolated OS; 2) Dr. Wu discussing features of RWD that make for an attractive choice for extending the survival curve; 3) Dr. Sheinson providing insights on methods for combining RWD with RCT data to extrapolate survival; and 4) Dr. Krivasi concluding the formal presentations with examples illustrating how RWD can supplement trial data in CEA models.