About this course

The following topics are presented during the course:

  • Using Stata
  • Causal concepts
  • Questionnaire design
  • Sampling
  • Measures of disease and associations
  • Cohort design
  • Case-control studies
  • Hybrid study design
  • Controlled studies
  • Diagnostic test properties and test evaluation
  • Validity in observational studies
  • Confounder bias: analytic control and matching
  • Linear and Logistic regression

Learning outcome

Through the course the students are expected to reach the following platforms:

Knowledge

  • Know the theoretical basis of planning and analysis of epidemiological studies.

Skills

  • Be able to plan an epidemiological study
  • Be able to perform basic analysis of data from epidemiological studies

  • Learning activities

    The course consists of three separate weeks of compulsory training, and work with assignments between these weeks.

    Course days are a mix of theoretical lectures (before lunch) and work with reading and assignments (after lunch), often with a common review of assignments at the end of the day. Lecturers will be available for questions every afternoon.

  • Teaching support

    Teachers are available all day when they teach. At the end of each day, the teacher will summarise the material and answer questions.

  • Syllabus

    1. Veterinary Epidemiologic research (Dohoo et al.) To be bought by the student or can be downloaded via link on Canvas
    2. Veterinary Epidemiology: An Introduction (Pfeiffer) (available through the course)

  • Prerequisites

    A basic knowledge of statistical concepts and methods (such as VET410 and a background in veterinary epidemiology from veterinary studies are required. Students without this background are recommended to read a short introductory text such as Veterinary Epidemiology. An introduction (Pfeiffer) before the course starts.

  • Recommended prerequisites

    Basic competence in biostatistics.

  • Assessment method

    Each course part will be evaluated either by assignments or discussions. Participants are evaluated as Pass/ Fail. All assignments must be handed in the day before the last day of the course.

  • About use of AI

    It will be possible to use AI to help write Stata code as well as to annotate do-files.

    Descriptions of AI-category codes.

  • Examiner scheme

    Assignments are discussed with all course teachers. There is no external examiner, but the course is based upon an international course given by Ian Dohoo over many years,

  • Mandatory activity

    Attendance is compulsory the three active weeks. An attendance of at least 75% is required to pass the course.

  • Notes

    Deadline for registration: 1st of February

  • Teaching hours

    Preparing for the course: 30 hours

    Lectures: 60 hours

    Assignments, individual and groups: 95 hours

    Discussions with teachers about assignments: 40 hours

  • Preferential right

    PhD students and research line students at NMBU VET

  • Reduction of credits

    No