Faculty of Science

Survival Analysis
Provider: Faculty of Science

Activity no.: 5570-20-07-31
	Enrollment deadline: 11/11/2020

Place

Department of Mathematical Sciences

Date and time

16.11.2020, at: 08:00 - 29.01.2021, at: 16:00

Regular seats

ECTS credits

7.50

Contact person

Nina Weisse E-mail address: weisse@math.ku.dk

Enrolment Handling/Course Organiser

Thomas Scheike E-mail address: thsc@sund.ku.dk

Written language

English

Teaching language

English

Semester/Block

Block 2

Scheme group

Exam requirements

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

Exam form

Written assignment

Exam form

Written assignment, 72 hours

Exam details

A takehome exam combining theoretical and practical work.

Exam aids

All aids allowed

Course workload

Course workload category	Hours
Lectures	28.00
Preparation	125.00
Exercises	21.00
Exam	32.00

Sum	206.00

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.

Formal requirements

This is a course for mathematical statistics students with the appropriate prerequisities in statistics and probability theory.

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.

Teaching and learning methods

4 hours of lectures and 3 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.

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