Syllabus for |
|
MVE210 - Linear mixed models for longitudinal data
|
| Linear mixed models for longitudinal data |
| |
| Syllabus adopted 2019-02-22 by Head of Programme (or corresponding) |
| Owner: MPENM |
|
|
7,5 Credits
|
| Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
| Education cycle: Second-cycle |
|
Major subject: Mathematics
|
|
Department: 11 - MATHEMATICAL SCIENCES
|
Teaching language: English
Application code: 20146
Open for exchange students: Yes
| Module |
|
Credit distribution |
|
Examination dates |
|
Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 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 programs
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
Examiner:
José Sánchez
Go to Course Homepage
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