Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Survival Analysis

Practical information
Study year 2016/2017
Block 2
Programme level Full Degree Master
Course responsibles
  • Thomas Scheike (4-766a75654275777066306d7730666d)
  • Torben Martinussen (3-7a736746797b746a34717b346a71)
  • Department of Mathematical Sciences
  • Department of Public Health
Course number: NMAK16019U

Course content

Survival analysis or failure time data analysis means the statistical analysis of data, where the response of interest is the time T from a well-defined time origin to the occurrence of some given event (end-point). In biomedicine the key example is the time from randomization to a given treatment for some patients until death occurs leading to the observation of survival times for these patients. The objective may be to compare different treatment effects on the survival time possibly correcting for information available on each patient such as age and disease progression indicators. This leaves us with a statistical regression analysis problem. Standard methods will, however, often be inappropriate because survival times are frequently incompletely observed with the most common example being right censoring. The survival time T is said to be right censored if it is only known that T is larger than an observed right censoring value. This may be because the patient is still alive at the point in time where the study is closed and the data are to be analyzed, or because the subject is lost for follow-up due to other reasons. 

The course gives a broad introduction to concepts and methods in survival and event history analysis. Topics covered include counting processes and martingales; the Nelson-Aalen and Kaplan-Meier estimators; the log-rank test; hazard regression models including Cox proportional hazards regression and additive hazards regression; goodness-of-fit tools based on martingale residuals; analysis of clustered survival data using frailty models and/or marginal models; competing risk models; statistical computing in R.

Learning outcome


   * A basic understanding of survival analysis techniques and when they need to be applied.

Skills: Ability to

   * Perform practical analyses of event type outcomes. Using regression
     models and non-parametric methods.  Validate the used models.

   * Understand and establish etimating equations in the context of
     event history data. Derive asymptotic properties based on estimating

Comptences: Ability to

   * explain and understand when survival analyses methods are needed.

   * derive asymptotic distributions for simple estimating equations.

   * to engage in collaborative work with other researchers in the context of
     survival analysis.

Recommended prerequisites

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MSc Programme in Statistics


Study Board of Mathematics and Computer Science

Course type

Single subject courses (day)


1 block


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Teaching and learning methods

4 hours of lectures and 2 hours of exercises per week for 7 weeks.


No restrictions/ no limitations




Category Hours
Lectures 28
Exercises 14
Preparation 132
Exam 32
English 206


Type of assessment

Written assignment, 3 days
A takehome exam combining theoretical and practical work.


All aids allowed

Marking scale

passed/not passed

Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Censorship form

No external censorship
One internal examiner


Same as ordinary exam except if ten or less students are signed up. In that case the format is changed to 30 min oral exam with 30 min preparation time, several internal examiners and all aids allowed.

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