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. The course deals with the following 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,
- The Expectation-Maximization Algorithm, Design Considerations, Case Studies.

**Syllabus**

The course is

- given every second year
- in the first half of spring
- jointly with Chalmers MVE210

#### Course information 2021

- Course coordinator: José Sánchez
- Schedule 2021

#### Course information 2019

- Course coordinator: Ziad Taib
- Schedule 2019

#### Course information 2017

- Course coordinator: Ziad Taib
- Schedule 2017

#### Course information 2015

- Course coordinator: Ziad Taib
- Schedule

#### Course information 2013

- Course coordinator: Ziad Taib
- Schedule

#### Course information 2011

- Course coordinator: Ziad Taib
- Schedule

#### Course information 2009

- Course coordinator: Ziad Taib
- Schedule