Advanced Topics in Survival Analysis
The most commonly used methods of dealing with survival, and other 'time to event' data, are based on the assumption of proportional hazards. This course is concerned with models for different types of data structure, or with different underlying assumptions.
Who Should Attend?
Statisticians in medical research in public sector institutions and in the pharmaceutical and related industries, who already have some familiarity with modelling survival data. In particular, some experience in using proportional hazards models will be advantageous.
How You Will Benefit
If you deal regularly with survival data and need more tools for their analysis, then this course will introduce you to a range of different survival analysis models.
What Do We Cover?
Practical work will be done in: SAS. Participants may instead use R.
Note: For practical work, participants must bring their own laptop with a fully licensed version of the software.
Dr Dave Collett is the author of two highly acclaimed textbooks: Modelling Survival Data in Medical Research and Modelling Binary Data. He was Associate Director of Statistics and Clinical Studies at NHS Blood and Transplant for 15 years, including five years as Director of their Clinical Trials Unit. Before that, he spent over 25 years at the University of Reading.
Dave will present a number of topics on day 3 of the course.
Related Courses: Non-Proportional Hazards: Modelling the Restricted Mean Survival Time using R; Advanced Survival Analysis using R; Survival Analysis for Medical and Health Professionals; Survival Analysis using R; Introduction to Survival Analysis; Bayesian Survival Analysis