An applied introduction to survival analysis for researchers who plan to use this method in their own research or studies.

The course focuses on key concepts and practical skills, with applied examples and datasets to teach implementation and model interpretation.

The course makes use of R and the survival/survminer packages. Some code in early chapters is also provided in Python using the lifelines package.

By the end of the course, participants should be able to:

• Recognise key features of time-to-event data (e.g., censoring)
• Visualise time-to-event data with survival curves
• Run a log-rank test to compare survival curves
• Build multivariate Cox regression models for more complex datasets
• Interpret the output and assumptions of the above statistical tests

• Know the limitations of the above statistical tests

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== Intended audience ==

This course is suitable for:

• postgraduate students, postdoctoral researchers, and other researchers working with experimental or observational data
• participants working on time-to-event data
• researchers who already understand linear models and want to extend their statistical toolkit
• participants with a working knowledge of R (and RStudio or similar IDE)

== Course fees ==

All fees are per full training day.

{| class="wikitable"
|-
! Category
! Fee
|-
| Industry full charge
| £130.00
|-
| Academic / Government / charity concessionary
| £65.00
|-
| Cambridge University staff members / postdocs / visitors
| £65.00
|-
| Cambridge University registered students
| Free
|-
| Cambridge University registered students non-attendance
| £22.00
|-
| Special events
| Per event
|}

Payment options will be provided in booking confirmation emails sent after registration.

More details…

== General information ==

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