Survival Analysis


04/10/2022 - 05/10/2022

Organised by:

Royal Statistical Society


Dr Dave Collett


Advanced (specialised prior knowledge)



View in Google Maps  (EC1Y 8LX)


12 Errol Street, London


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.


£599.76 to £832.32 (including VAT)

Website and registration:


Greater London


Quantitative Data Handling and Data Analysis, Survival analysis , Cox regression model , Survival data , Censoring , Frailty models

Related publications and presentations:

Quantitative Data Handling and Data Analysis

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