Survival Analysis

Date: Tuesday 05 October 2021 9.30AM - Wednesday 06 October 2021 4.30PM
Location: London
CPD: 12.0 hours
RSS Training
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Level: Professional (P)


Standard methods of survival analysis based on the Kaplan-Meier estimate of a survivor function, the log rank test and Cox regression modelling are widely used in many different areas of application.   But often, the assumptions that underlie these techniques may not be valid, or the data structure may be more complex.  Extensions of these basic methods allow particular features of data that occur in practice to be handled appropriately.  This course will begin with an overview of standard methods and then move on to some of the more advanced techniques.  Their practical application will be illustrated using SAS and R.

Please note: Bookings will close 4 working days before the course start date or when the course has reached its maximum capacity.

 
Level: Professional (P)


Standard methods of survival analysis based on the Kaplan-Meier estimate of a survivor function, the log rank test and Cox regression modelling are widely used in many different areas of application.   But often, the assumptions that underlie these techniques may not be valid, or the data structure may be more complex.  Extensions of these basic methods allow particular features of data that occur in practice to be handled appropriately.  This course will begin with an overview of standard methods and then move on to some of the more advanced techniques.  Their practical application will be illustrated using SAS and R.
 

Learning Outcomes

An appreciation of how the methods of survival analysis can be used in a variety of situations.


Topics Covered

Overview of standard methods for summarising survival data and the Cox regression model.  Types of censoring in survival data, including interval and dependent censoring.  Time dependent variables and the counting process formulation of survival data.  Parametric models for survival data, including flexible models based on splines.  Incorporating random effects into a survival analysis; frailty models.  Analysis of data where there is more than one type of event; models for competing risks.  Detecting and handling non proportional hazards. 


Target Audience

Statisticians and epidemiologists in public sector research organisations, pharmaceutical companies and related organisations.  University research students and fellows.


Assumed Knowledge

Some familiarity with basic methods for summarising survival data, including estimates of the survivor function and the log rank test.  Some experience in using the Cox regression model would be advantageous.

 

Dr Dave Collett

Dave Collett obtained his first degree at the University of Leicester, before going on to complete an MSc in statistics at the University of Newcastle and a PhD in statistics at the University of Hull.  Dave was a lecturer and senior lecturer in the Department of Applied Statistics at the University of Reading for over 25 years, including eight years as head of that department.  In 2003, he was appointed Associate Director of Statistics and Clinical Studies at NHS Blood and Transplant and held a visiting chair in the Southampton Statistical Sciences Research Institute, University of Southampton, until his retirement. 

 

Fees

   

Registration before
5 September 2021

 

Registration on/after
5 September 2021

                                  


Non Member 

RSS Fellow 

RSS CStat/Gradstat/Data Analyst 
also MIS & FIS

 

£611.00+vat 

£520.00+vat 

£490.00+vat

£680.00+vat 

£577.00+vat 

£543.00+vat

 
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