Survival analysis
An introduction to the core concepts, methods, and software of time-to-event analysis — for beginners in survival analysis.
What this course covers
Register interest
Time-to-event analysis examines data such as mechanical failures, disease onset, or organism death. This introductory course provides the core concepts, methods, and software training for beginners in survival analysis, combining statistical theory with practical applications.
Lectures are paired with computer practicals; the primary software is Stata (version 14+), with full R solutions provided.
Full course title: Survival analysis: An introduction to concepts, methods & software
Try it in your browser: the survival DGM explorer — see hazard and survival functions update live as you change the model.
At a glance
What you'll learn
Basic concepts
Risk-time, timescales, and censoring — the foundations of time-to-event data.
Rates & survival
Rates, survival, Kaplan-Meier estimation, and comparing survival between groups.
Hazards
The hazard and cumulative hazard, and how they describe the event process over time.
Parametric & Cox models
Parametric survival models and the Cox proportional hazards model.
Proportional hazards
The proportional hazards assumption and how to handle non-proportional hazards.
Survival analysis in Stata
Carrying out the full workflow in Stata, with R solutions provided.
Audience & prerequisites
Who it's for
Epidemiologists, statisticians, physicians, public health specialists or anyone with an interest in methods for survival analysis.
Prerequisites
Basic knowledge of the fundamentals of epidemiology and biostatistics, and comfort fitting and interpreting statistical models in epidemiology (e.g. linear and logistic regression). Prior basic knowledge of Stata is assumed.