Survival Analysis: Why? What? How?

Description and Objectives:

Survival analysis is a pillar in many health technology assessments, especially in the field of oncology. This half day course will provide participants with a comprehensive introduction to the basics of survival analysis. It will cover topics ranging from the interpretation of a Kaplan-Meier curve to addressing complex hazard patterns, using hands-on exercises to allow attendees to practice the application of different survival extrapolation methods.

The course will start by introducing the Kaplan-Meier (KM) curve and the interpretation of such curves. Theoretical concepts will be illustrated with real examples and participants will get hands-on experience with plotting KM curves, fitting a Cox model and evaluating the assumption of proportional hazards. This will allow practice with interpreting results of a trial dataset.

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 some simple code with which to explore multiple parametric models themselves. A completed code set will be provided following 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:

  1. Identify contexts in which survival analysis is relevant and appropriate

  2. Plot and interpret KM curves

  3. Assess the assumption of proportional hazards

  4. Extrapolate observed survival using standard parametric models

  5. Understand complexity in survival data and suggest appropriate methods for analysis

  6. Discuss the impact of choices made in the survival analysis

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