Statistics - Survival Analysis
1 & 2 October 2025
Royal Statistical Society
Summary
- Certificate of Attendance - Free
- Tutor is available to students
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Location & dates
End date: 02/10/2025
Additional info: Run over 2 full days, from 9:30am to 5:00pm. Lunch is provided
London
EC1Y8LX
United Kingdom
Overview
This course is being held on 1 & 2 October 2025
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.
Certificates
Certificate of Attendance
Hard copy certificate - Included
Description
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. Model selection strategy. Incorporating time dependent variables and the counting process formulation of survival data. Parametric models for survival data, including flexible models based on splines. Detecting and handling non proportional hazards. 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. Dependent censoring.
Who is this course for?
Statisticians and epidemiologists in public sector research organisations, pharmaceutical companies and related organisations. University research students and fellows.
Requirements
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.
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