Survival and Event History Analysis

Course content

The course introduces statistical concepts and methods for analyzing time-to-event (survival) data obtained from following individuals until a particular event occurs, or they are lost to follow-up. We will illustrate the use of modern tools for time-to-event analysis and discuss interpretation and communication of results. The course provides practical experience with real health science data.

The content includes: censoring and truncation; Kaplan-Meier estimation; log-rank tests; Cox regression; Aalen's additive regression model; competing risks; clustered data.

Education

MSc in Public Health Science - elective course

Not open for BSc students

 

Learning outcome

After completing the course the student is expected to:

Knowledge

  • Distinguish methods for analysis of time-to-event data from other types of measurements.
  • Understand the concepts of censoring and truncation.
  • Explain central survival analysis concepts such as hazard, survival and cumulative incidence and their relationships.
  • Describe and compare models for time-to-event data. Illustrate how the models can be applied to epidemiological or public health data.
  • Understand and recognize common pitfalls specific to time-to-event data.

 

Skills

  • Determine the proper statistical method to address a specific scientific question from a given time-to-event data set. This includes understanding the underlying assumptions of the method and identifying violations of these.
  • Perform time-to-event analysis with modern techniques using statistical software. Assess the fit of the model.
  • Interpret the results reported by statistical software. Communicate the results and conclusions of a time-to-event analysis in a clear and precise way.
  • Write a statistical report with a well-defined problem statement, method and result presentations, discussion and conclusion.
  • Critically read and review reports or articles addressing epidemiological and public health questions by time-to-event analysis.

 

Competencies

  • Identify and conceptualize questions encountered in the professional life of a public health researcher that can be addressed by time-to-event studies.
  • Independently design, carry out and communicate time-to-event studies.
  • Give advice and take active part in collaborations where decisions are based on the statistical analysis of time-to-event data.

Class instruction and home assignments.

The course is developed to follow after the course in statistics from the MSc in Public Health Science that contains an introduction to survival analysis.

Passed course in statistics SFOA09001U/E from the MSc in Public Health Science.

Students at the Master's programmes in Health Informatics, Global Health, Human Biology and Health Science with a strong interest in statistics prepared to read up on the basics of survival analysis are welcome in the course and can apply as credit transfer students.
For questions on prerequisites, contact the course responsible.

Collective
Continuous feedback during the course of the semester
ECTS
5 ECTS
Type of assessment
Continuous assessment
Written assignment
Project report at the end of the course.
Aid
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Criteria for exam assessment

To pass the exam, the student must be able to:

Knowledge

  • Distinguish methods for analysis of time-to-event data from other types of measurements.
  • Understand the concepts of censoring and truncation.
  • Explain central survival analysis concepts such as hazard, survival and cumulative incidence and their relationships.
  • Describe and compare models for time-to-event data. Illustrate how the models can be applied to epidemiological or public health data.
  • Understand and recognize common pitfalls specific to time-to-event data.

 

Skills

  • Determine the proper statistical method to address a specific scientific question from a given time-to-event data set. This includes understanding the underlying assumptions of the method and identifying violations of these.
  • Perform time-to-event analysis with modern techniques using statistical software. Assess the fit of the model.
  • Interpret the results reported by statistical software. Communicate the results and conclusions of a time-to-event analysis in a clear and precise way.
  • Write a statistical report with a well-defined problem statement, method and result presentations, discussion and conclusion.
  • Critically read and review reports or articles addressing epidemiological and public health questions by time-to-event analysis.
  • Category
  • Hours
  • Class Instruction
  • 20
  • Exam
  • 25
  • Preparation
  • 93
  • English
  • 138