Course syllabus for Linear mixed models for longitudinal data

The course syllabus contains changes
See changes

Course syllabus adopted 2019-02-22 by Head of Programme (or corresponding).

Overview

  • Swedish nameLinear mixed models for longitudinal data
  • CodeMVE210
  • Credits7.5 Credits
  • OwnerMPENM
  • Education cycleSecond-cycle
  • Main field of studyMathematics
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 20146
  • Open for exchange studentsYes

Credit distribution

0108 Examination 7.5 c
Grading: TH		 7.5 c
  • 18 Mar 2021 am J
  • 09 Jun 2021 am J
  • 25 Aug 2021 am J

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Students are expected to be familiar with basic statistical inference and to have taken some course in regression and analysis of variance, like Linear statistical models.

Aim

This course is an introduction to the area of mixed models which has become a necessary tool for treating real life situations with e.g. random effects, correlated observations and missing data. The emphasis is on longitudinal data and on how to use SAS and R to analyse mixed models.

Learning outcomes (after completion of the course the student should be able to)

  • use the mixed model framework
  • recognize common study designs and models with longitudinal data or otherwise correlated observations
  • conduct an appropriate statistical analysis of models covered in the course using standard software

Content

This course is an introduction to the area of mixed models which has become a necessary tool for treating real life situations with e.g. random effects, correlated observations and missing data. The emphasis is on longitudinal data and on how to use SAS and R to analyse mixed models.

Main topics: Exploratory Data Analysis, Estimation of the Marginal Model, Inference for the Marginal Model, Inference for the Random Effects, Fitting Linear Mixed Models with SAS, General Guidelines for Model Building, Exploring Serial Correlation, Local Influence for the Linear Mixed Model , The Heterogeneity Model, Conditional Linear Mixed Models, Exploring Incomplete Data, Joint Modeling of Measurements and Missingness, Simple Missing Data Methods, Selection Models, Pattern-Mixture Models, Sensitivity Analysis for Selection Models, Sensitivity Analysis for Models, How Ignorable is Missing at Random?, The Expectation-Maximization Algorithm, Design Considerations, Case Studies

Organisation

Lectures and computer exercise classes.

Literature

Linear Mixed Models for Longitudinal Data Series: Springer Series in Statistics by Verbeke, Geert, Molenberghs, Geert and handouts.

Examination including compulsory elements

Home assignments and written final examination

The course syllabus contains changes

  • Changes to examination:
    • 2021-04-14: Exam date Exam date changed by Elisabeth Eriksson
      [33089, 53299, 3], New exam for academic_year 2020/2021, ordinal 3 (not discontinued course)
  • Changes to course rounds:
    • 2020-12-07: Examinator Examinator changed from Ziad Taib (ziad) to José Sánchez (sanchezj) by Viceprefekt/adm
      [Course round 1]