Watch to hear the techniques of survival analysis, their application in economic evaluations, and lessons learned from conducting survival analysis on immature data, addressing the challenges of analyzing different outcomes.

Health economic models often rely on estimates of time-to-event for outcomes – e.g. time to disease progression, time to first stroke, time to death – to estimate costs and health outcomes over lifetime. Time-to-event, or “survival”, analysis is used to provide estimates of survivor functions and event rates that inform these models. As well as characterizing event rates over an observed period, such as the duration of a clinical trial, survival analysis techniques are employed to extrapolate over lifetime to generate estimates of mean survival benefit.

Estimates of the cost-effectiveness of an intervention can be sensitive to the methods applied in modeling and extrapolating survival data. Standard parametric models are preferred when the underlying distribution of survival times is well-understood and can be adequately described by a specific parametric form; advanced survival methods are needed for more complex or heterogeneous data.

Key Topics:

• What is survival analysis and how is it used in economic evaluations


• Recent applications in the field of survival analysis


• Lessons learned when conducting survival analysis on immature data


• Challenges in conducting survival analyses on different outcomes (overall survival, progression-free survival, duration of treatment)