HES9325 – Register-based epidemiology

Course content

The course will focus on the use of epidemiologic methods relevant for register-based data. Official health registers in Norway, and other registers important for health research will be presented. The characteristics of the different registers will be highlighted by using examples. The course will give an overview of Norwegian laws and regulations pertaining to register-based data. It is of key importance to know the origin of your register data, as the primary purpose of most register data is not research and this has implications for both the type and the quality of the data. Both data quality assessment and data management are important parts of the course. The course will also teach you how to work with large data sets, to combine and analyze them and how to search for errors.

Learning outcome

Learning outcome

The course will give you knowledge about:

  • registers in Norway relevant for health research
  • epidemiologic methods which can be used on register-based data
  • Norwegian laws and regulations pertaining to register-based data
  • strengths and weaknesses of register-based data
  • systematic errors which can occur when using register data (for example left truncation and "immortal time bias")

Skills

The course will give you the skills to:

  • be able to discuss data quality in relation to the origin of the data (how and by whom it was registered)
  • be able to take part in planning, carrying out analyzes of register based data (both one register, registers in combination, or in combination with other types of data such as health surveys or clinical data)
  • be able to work with large register-based merged datasets and perform error search
  • define research questions based on register-data
  • critically evaluate scientific papers that have used data from registers

General competence

The students are expected to obtain an epidemiologic competence which make them able to contribute in planning and carrying out epidemiological studies based on register data. You will also learn how to work with large combinations of data from different registers, and gain enough competence to give feedback on register based research.

Admission

PhD candidates at UiO will get first priority to the course. Maximum number of participants is 30.

Applicants who are admitted to a PhD programme at UiO apply in StudentWeb
Applicants who are not admitted to a PhD programme at UiO must apply for a right to study in S?knadsWeb before they can apply for PhD courses in medicine and health sciences.  External applicants should apply for a right to study 3 weeks before the course application deadline. 

Applicants will be notified by email 1 - 2 weeks after the final date for registration.

 

Prerequisites

Formal prerequisite knowledge

General courses in statistics and epidemiology are expected.

Recommended previous knowledge

The candidate should have experience with STATA or other similar statistical packages.

Teaching

The course consists of lectures and is organized as full day teaching over 4 days.

Note: Autumn semester 2020 the teaching will run digitally. Detailed information will come.

You have to participate in at least 80 % of the teaching to be allowed to take the exam. Attendance will be registered.

Examination

A take-home exam will be given at the end of the course.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Resit an examination

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Evaluation

The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Facts about this course

Credits
3
Level
PhD
Teaching
Every spring
Examination
Every spring
Teaching language
English