How to analyze time-to-event data
Programming skills with R, e.g. course Introduction to R and basic knowledge of regression models and hypothesis testing, e.g. course Introduction to Statistics. Basic knowledge on applying ggplot2 functions is advantageous but not mandatory (e.g. course Graphics with R).
The participants will be taught the theoretical backgrounds on time-to-event analysis and how to apply them in R. Time-to-event data can be found in various settings, e.g. time until death, recurrence of a tumor, discharge from a hospital or time until a machine breaks. They will learn when to apply survival analysis, what survival analysis method to use in different situations and how to visualize and interpret the results of survival analysis methods. This includes Kaplan-Meier estimation of the survival curve and Cox Proportional Hazards model. Finally, parametric regression models for survival analysis are presented. All topics are accompanied by examples and hands-on exercises in R. Accompanying packages in R for survival analysis will be introduced.
This course on survival analysis covers three different parts:
- Introduction to time-to-event analysis: This includes general assumptions, types of data, censoring, survival time, survival time as a function.
- Non-parametric estimation of the survival function. This includes Kaplan-Meier estimation of the survival function, visualization of the survival curve, comparison of survival curves and Kaplan-Meier estimation in R.
- Cox proportional hazards model: This includes when to apply Cox models, model specifications and assumptions, model inference, interpretation of the results of the Cox models, testing the model assumptions and how to run Cox models in R.
- Parametric regression models: AFT models: Weibull regression, model specifications and assumptions, model inference, interpretation of the results of the Weibull model, testing the model assumptions and how to run Weibull models in R.
The course consists of theoretical lessons on survival analysis methods, how to apply survival analysis methods and how to visualize survival curves in R. Theoretical lessons will be followed by hands-on examples with best-practice solutions in R.
- Duration: 2 Days
- Language: English
- This course will be offered either on campus (in person), or online
- For online courses we use the software Zoom.
- Material for the course can be found here*.
- Please be aware that the materials will be updated shortly before the next course.
Dates and Application:
- Courses provided for Helmholtz Munich:
* Links marked with * are only available for Helmholtz Munich staff.