Introduction to Survival Analysis
Survival data arise in a literal form from trials concerning life-threatening conditions, but the methodology can also be applied to other waiting times such as the duration of pain relief. This course discusses both the analysis and the design of clinical trials in which the response variable is a survival time.
This course emphasises the practical aspects of analysing survival data and interpreting models, but the underlying theory is explained as appropriate. In practical sessions participants apply the methods covered to a simulated clinical trial. The statistical package SAS is used to illustrate the methodologies in the presentations and for practical work. R and Stata may also be used for practical work.
Most of the topics on the course are covered in the book Modelling Survival Data in Medical Research, by Dr Dave Collett (Associate Director of Statistics and Clinical Audit at NHS Blood and Transplant), published by Chapman and Hall/CRC. A copy will be given to each participant.
Who Should Attend?
Statisticians engaged in medical research in public sector industries and in the pharmaceutical and related industries, who have little or no experience of dealing with survival data. No previous experience of R, SAS or Stata is required.
How You Will Benefit
This course will give a thorough introduction to survival analysis from basic methods through to commonly-used modelling approaches.
What Do We Cover?
- Estimating the survivor function; the Kaplan-Meier estimate
- Comparison of groups: the log-rank and Wilcoxon tests
- The Cox proportional hazards model
- The Weibull and Gompertz parametric models
- Some model-checking procedures
- Sample size considerations.
This course has practical exercises written for: R, SAS, Stata
Cost of this course includes a course textbook