Determining the clinical value of a product is a key challenge in health technology assessments (HTAs) and survival benefit can be a major component of the clinical value, especially in fields such as oncology. However, survival benefit is typically established based on trial data with limited follow-up, thus extrapolation to a longer time horizon is often required in order to estimate the full value of the product. This course will provide an introduction to why one would want to conduct survival analyses, what methods are available, and how to use these methods. It will cover how to interpret Kaplan-Meier curves, and how to extrapolate observed survival, or any other time to event outcome of interest, to a time horizon of use for HTAs. Participants will explore standard parametric extrapolation methods as well as receiving a primer on alternative methods that can account for more complex survival data.
Special attention will be given to the hazards underlying the survival curves and these hazard curves will be used as a basis to discuss and compare different standard parametric models. Model selection is a key element of survival analysis, and choices made in the model selection can have a strong impact on health technology assessments. The discussion will investigate technical aspects of the models, but also relate this to clinical interpretation. Participants will be provided with a dataset and a simple code basis to explore multiple parametric models themselves. A completed code set will be provided after the hands-on exercises.
Once the standard parametric models have been explored, the concept of complex hazard functions will be introduced as well as methods for modelling more complex survival data. The course will be wrapped up by highlighting the importance of validating survival analyses.
Completing this course will allow participants to: