Training Course Details

Survival Analysis with R

Survival Analysis with R

Course Level: Intermediate

This course is a practical introduction to some of every day and more sophisticated tools used for the analysis of survival data.

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Course Details

  • Course Outline
  • Learning Outcomes
  • Materials
  • Prior Knowledge

Course Outline

  • Introduction to survival data: Kaplan-Meier curves and the log-rank test
  • The Cox proportional hazards model: Implementation, interpretation and limitations
  • Residual analysis: Model checking and model comparison
  • Time-dependent covariates
  • Models: Parametric survival models using the Weibull distribution
  • Advanced topics: Frailty and joint modelling of longitudinal and survival data.

Learning Outcomes

By the end of the day participants will…

  • be able to use a Kaplan-Meier curve to estimate the survival function from lifetime data
  • be familiar with Cox proportional hazards models, which are used when investigating the association between predictor variables and the survival distribution
  • be able to cross-check different models to find the optimum, using residual analysis
  • have learned examples of parameterised survival models
  • be able to acknowledge differences between parameterised models and Cox models


Prior Knowledge

It is expected that participants have basic R experience. The course will be hands-on and applied with short introductory lectures to each topic, followed by practical sessions working with real survival datasets, available within R.

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