HEL-8003 Mixed Models - 2 stp

During the course the following topics are discussed:

• Basic principles of multilevel analysis
• Background of multilevel analysis
• Example of a multilevel analysis with a two level structure
• Example of a multilevel analysis with a three level structure
• Multilevel analysis with dichotomous outcome variables
• The use of multilevel analysis in longitudinal studies
• Generalised estimating equations
• Alternative models for longitudinal data analysis
• Sample size calculations
• Missing data
• Software for mixed models

Other applicants apply for the right to study by June 1st for courses that are taught in the fall semester and December 1st for courses that are taught in the spring semester. Application is sent through SøknadsWeb. Please use the appliction code "9301 - Singular courses at the PhD level". If granted admission to the course students must register for class and exam in Studentweb by September 1st for autumn semester and February 1st for spring semester.

PhD students and students at the Student Research Programme, or holders of a Norwegian master´s degree of five years or 3+2 years (or equivalent) may be admitted.

External PhD students and students at other Student Research Programmes, must upload a document from their university stating that they are registered students.

Having attended the course and completed the exam the students will obtain the following learning outcomes:

Knowledge and understanding:

• Understand the basic principles of multilevel analysis.
• Understand the difference between mixed models with continuous, binary and count outcomes.
• Understand the difference between longitudinal analyses with continuous, binary and count outcomes.
• Understand generalised estimating equations (GEE) models.
• Interpret results from mixed models and GEE models.
• Know how to handle missing data in mixed models and GEE.

Skills:

• Be able to use a statistical package to analyse data using mixed models and GEE models.
• Perform sample size calculations for mixed model analysis and for longitudinal studies.
• Separate between the different correlations structures in GEE models

General Competence:

• Know how to interpret results from mixed models and GEE models.
• Know about different softwares for mixed models and GEE models.