Advanced Survival Analysis using R
(online course)

Overview
The most commonly used methods of dealing with survival and other time-to-event data are based on the assumption of proportional hazards. But often this assumption may not be tenable, or the data structure may be more complex. This course is concerned with models for different types of data structure, or with different underlying assumptions.
Examples used will be drawn from a variety of applications in medicine and health.
Practical work will be based around the statistical software R; see https://www.r-project.org/.

​Presenters
Sandro Leidi and James Gallagher

Delivery Mode
All training is online and will be delivered live each day between 09:00 and 17:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter.​  We also use Zoom meetings rather than webinars to encourage further interaction during an online course.

Who Should Attend?
Statisticians working in medical research in public sector institutions and in the pharmaceutical and related industries, who already have some familiarity with modelling survival data.
Participants will be assumed to have a working knowledge of
  • Modelling survival data; in particular the Cox proportional hazards regression model
  • The R statistics software.

How You Will Benefit
If you deal regularly with survival data and need more tools for modelling it, then this course covers a range of different survival analysis models.

What Do We Cover?
  • A review of the Cox proportional hazards regression model
  • The counting process format
  • The Weibull proportional hazards regression model
  • Accelerated Failure Time models
  • Time varying covariates
  • Non-proportional hazards
  • Frailty models, i.e. inclusion of random effects
  • Competing risks
  • R packages for fitting the above models, including survival, coxme, parfm and cmprsk.
The course does not cover multi-state models.

Software
Practical work will be done in R.
Note: For practical work, participants must download and install a number of CRAN packages in R.  This must be done prior to the start of the course.

Extra Information
Related courses: Non-Proportional Hazards: Modelling the Restricted Mean Survival Time using R;  Advanced Topics in Survival Analysis; Introduction to Survival Analysis; Survival Analysis for Medical and Health Professionals using R.
Course Dates
14-15 May 2025

Venue: Online
Duration: 2 days
Price: £558

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