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Survival Analysis
Provider: Faculty of Science

Activity no.: 5570-18-07-31 
Enrollment deadline: 19/11/2018
Tilmelding : Survival Analysis
ECTS credits7.50
PlaceDepartment of Mathematical Sciences
Universitetsparken 5, 2100 København Ø
Date and time19.11.2018, at: 09:00 -
27.01.2019, at: 00:00
Regular seats50
ECTS credits7.50
Contact personNina Weisse    E-mail address: weisse@math.ku.dk
Enrolment Handling/Course OrganiserThomas Scheike    E-mail address: thsc@sund.ku.dk
Teaching languageEnglish partially in English
Semester/BlockBlock 2
Scheme groupC
Exam formWritten assignment
Exam formWritten assignment, 72 hours
Exam detailsWritten assignment, 3 days. A takehome exam combining theoretical and practical work. All aids allowed. 7-point grading scale. No external censorship.
Course workload
Course workload categoryHours
Lectures28.00
Exercises14.00
Preparation132.00
Exam32.00
Sum206.00
Learning outcome
Knowledge:
* 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.
* Use basic counting process calculus to derive properties of estimators and relationships between key model quantitites.

Comptences: Ability to
* explain and understand when survival analyses methods are needed.
* Identify, justify, analyse and report failure time models in practical settings.
* to engage in collaborative work with other researchers in the context of survival analysis.

Target group
Knowledge:
* 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.
* Use basic counting process calculus to derive properties of estimators and relationships between key model quantitites.

Comptences: Ability to
* explain and understand when survival analyses methods are needed.
* Identify, justify, analyse and report failure time models in practical settings.
* to engage in collaborative work with other researchers in the context of survival analysis.

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

The exercises will consider both theoretical problems as well as practical analyses of data. Here the students will have to participate actively, that is take active part in working on the problems in class, and take turns demonstrating the solutions to the different problems.

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, goodness-of-fit tools and marginal models; competing risk models; statistical computing in R.

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