Survival analysis can provide the data to inform more effective health economic models


Economic evaluations for health technology assessments (HTAs) often rely on estimates of “time-to-event” for patient outcomes, such as time to disease progression, time to first stroke, or time to death to assess the costs and benefits of a treatment or intervention. Analysis of time-to-event data – also known as survival analysis – can provide estimates of survivor functions and event rates that inform these economic analyses. As well as characterising event rates over an observed period of time, such as the duration of a clinical trial, survival analysis techniques are used to extrapolate the results over a lifetime to generate estimates of mean survival benefit.

Estimates of an intervention’s cost-effectiveness can be sensitive to the methods applied in modelling and extrapolating survival data. Typically, these projections are performed using various parametric models, with each model making different assumptions about the underlying hazard function. Advanced techniques have recently been developed to account for complex challenges such as interval censoring, cured patients and treatment crossover.

Join us to review the basics of survival analysis and how it can be used in economic evaluations. Attendees will learn about:

  • Current guidelines on extrapolating time-to-event data from clinical trials
  • Challenges when conducting survival analysis on immature data
  • Recent developments in the field of survival analysis

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Dhvani Shah

Dhvani Shah, MS

Principal, Global Health Economics, ICON

Dhvani Shah has over 10 years of experience in health economics and outcomes reserach, and has led numerous evidence generation activities to demonstrate and communicate product value.  At ICON, she is responsible for preparing HEOR research and value strategies, developing economic models for HTA submissions and conducting evidence synthesis activities.