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Advanced biostatistics and epidemiology, 7.5 Credits

Swedish name: Avancerad biostatistik och epidemiologi

This syllabus is valid: 2016-08-29 and until further notice

Course code: 3FH065

Credit points: 7.5

Education level: Advanced level

Main Field of Study and progress level: Public Health: second cycle, has second-cycle course/s as entry requirements

Grading scale: VG Pass with distinction, G Pass, U Fail

Responsible department: Epidemiology and Global Health

Established by: Programme council for the International Master Programme in Public Health (PRPH), 2016-05-18


The focus in the course is on the application of statistical approaches in epidemiological research, which covers the aspect of data management and selection of appropriate statistical model. Regression analysis is a statistical technique used for analysing the relationship between outcome (dependent variable) and explanatory variables (independent variables). In this course, several regression models will be described and applied. The course will start with a repetition of linear regression model which deals with continuous outcome variable. Subsequently, several other regression models will be introduced and discussed, including: binary logistic regression (for binary outcome variable), Poisson regression (for “count” outcome variable), and Cox regression (for “time to event” outcome variable). Basic concepts in survival analysis, including censoring, survival function and hazard function, will also described and discussed. Further, different advanced epidemiological methods to analyse causation such as mediation analysis, propensity score matching, and instrumental variable analysis will be taught. The concept of multilevel analysis will also be introduced and discussed in the course. Epidemiological research articles will be used for group discussion, where students focus on the application of the statistical and epidemiological methods. In this course, the theoretical part will be illustrated using data and analytical results, and students will practice the application of different analytical approaches in computer exercises.

Expected learning outcomes

Knowledge and understanding

After this course, students are expected to:
  • Comprehend and clarify the role of statistical theory in epidemiological research;
  • Comprehend and clarify basic theory about different regression models (linear regression, logistic regression, Poisson regression, and Cox regression) and have knowledge about basic concepts in survival analysis; and
  • Comprehend advanced epidemiological methods to analyse causation.

Skills and abilities

After this course, students are expected to be able to:
  • Perform and analyse data using different regression models (linear regression, logistic regression, Poisson regression, and Cox regression);
  • Perform advanced epidemiological analyses to analyse causation; and
  • Interpret, clarify and discuss the result of such analyses correctly.

Judgement and approach

After this course, students are expected to:
  • Critically discuss and evaluate the application of statistical methodology in epidemiological and health science articles

Required Knowledge

For non-programme students applying as single-course students, the requirements are 120 ECTS, of which a minimum of 30 ECTS are within one of the following: health sciences, environmental health or social sciences. To be admitted to the course tha applicant must have passed the course 3FH072 Biostatistics and the course 3FH038 Epidemiology or have equivalent qualifications.

English proficiency equivalent to English A/5 from Swedish Upper secondary education. IELTS (Academic) with minimum score 5.5 and no individual score below 5.0. TOEFL (Paper based) with minimum score 530 and minimum TWE 4. TOEFL (Internet based) with minimum score 72 and minimum Written 17.
Basic entrance requirements for higher studies in Swedish language proficiency is also required if the course is taught in Swedish.

Form of instruction

Teaching is performed through plenary lectures, web-lectures, group exercises, practical computer exercises and seminars. Teaching is given in English.

Examination modes

The examination consists of two parts: (1) Assignment and (2) Written exam. Only grades of Fail (U) and Pass (G) will be awarded for the assignment. The written exam will be awarded the following grades: Fail (U), Pass (G) or Pass with Distinction (VG). The whole course is graded Fail (U), Pass (G) or Pass with Distinction (VG). In order to be awarded Pass for the entire course, it is required that both examination parts must be passed. To be awarded Pass with distinction, it is required that the written exam is awarded Pass with distinction. Students who do not pass the regular examination will have a new examination within 3 months after the first examination.

If there are special reasons, the examiner has the right to decide whether another form of examination can be used. A student who has failed two tests for a course, are entitled to have another examiner appointed, unless there are specific reasons against it. A written request is submitted to the director of studies.

Other regulations

Students have the right to examine whether previous education or equivalent knowledge and skills acquired can be credited for the corresponding course at Umeå University. Application is submitted to Studentcentrum/Examina. Details on crediting can be found at Umeå University’s student web,, and the Higher Education Ordinance (Chapter 6). A refusal of accreditation may be appealed against to the University Appeals Board. This applies to the whole or part of the application for accreditation is refused.


Valid from: 2016 week 36

Course literature

Vach Werner
Regression models as a tool in medical research
Boca Raton : CRC Press : 2013 : xxi, 473 pages :
ISBN: 9781466517486
Search the University Library catalogue

Additional reading material

Dupont William D.
Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data
2. ed. : Cambridge, UK : Cambridge University Press : 2009. : xx, 522 s. :
ISBN: 9780521849524 (hardback)
Search the University Library catalogue