Description

Survival analysis, also known as failure time analysis, is a set of statistical tools used to analyse time to event data. Examples of time to event data: time until tumour recurrence, time until death after some treatment, time until a machine failure. Censoring is a common feature in survival analysis in which time to event is not observed for many reasons, for example, the study has been terminated before all subjects have experienced the event of interest, or subjects might leave the study before experiencing the event of interest.

There are many interesting theoretical and applied topics students may want to work on, where implementation can be done using the statistical package R.

Prerequisites

Statistical Inference II and Statistical Modelling II. Familiarity with the statistical software R is essential.

References

• Survival analysis: Wikipedia.
• James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning with Applications in R (Chapter 11), 2021. [PDF]
• Survival analysis: Part I, Part II, Part III, Part IV.
• Collett, D. Modelling Survival Data in Medical Research, Chapman & Hall/CRC, 2015.
• Moore, D.F.F. Applied Survival Analysis Using R, Springer, 2016.
• Smith, P.J. Analysis of Failure and Survival Data, Chapman and Hall/CRC, 2002.
• Kalbfleisch, J.D., Prentice, R.L. The Statistical Analysis of Failure Time Data, Wiley-Blackwell, 2002.
• Lawless, J.F. Statistical Models and Methods for Lifetime Data, Hoboken: John Wiley and Sons, 2003.
• Pintilie, M. Competing risks: a practical perspective, Chichester: John Wiley & Sons, 2006.