The aim of this course is to introduce participants to the statistical analysis of time-to-event outcomes. The course will be explanatory rather than mathematically rigorous. Sufficient detail will be given such that the participants will have a clear view on the different survival analysis approaches, and how they should be used in practice. To this end, the majority of the concepts introduced in the course will be illustrated in the R statistical language using package Rcmdr.
The course consists of five parts:
- In Part I we refer to the special characteristics of event time data (e.g., skewness, censoring, truncation).
- In Part II and III we present standard statistical tools for their analysis, including among others the Kaplan-Meier and Breslow estimators for the survival function, and the log-rank and Gehan-Wilcoxon tests.
- Part IV introduces regressions models for time-to-event data, such as the accelerated failure time and proportional hazard models.
- Part V focuses mainly on the Cox model and considers several extensions; in particular, the use of stratified Cox models, the analysis of clustered time-to-event data, and the handling of time-dependent covariates and competing risks problems.
Relevant Literature:
- Kalbfleisch, J. and Prentice, R. (2002). The Statistical Analysis of Failure Time Data, 2nd Edition. New York: Wiley.
- Kleinbaum, D. and Klein, M. (2005). Survival Analysis – A Self-Learning Text, 2nd Edition. New York: Springer-Verlag.
- Therneau, T. and Grambsch, P. (2000). Modeling Survival Data: Extending the Cox Model. New York: Springer-Verlag.
This course is equivalent to the Erasmus Summer Programme course Survival Analysis (ESP28) in August.