Linear Mixed Models for Longitudinal Data, 7.5 scores, spring 2017

Lectures:

  Alexandra Jauhiainen

  725518416

  Alexandra.jauhiainen@astrazeneca.com

  Ziad Taib

  0707655471

  ziad@math.chalmers.se

Computer projects:

  Trasieva, Teodora

  teodora.sheytanova@astrazeneca.com

  0317761000

  Marco Longfils

  0317723574

  longfils@chalmers.se

 

Schedule:

  Time Edit

 

Course summary and Objectives: The purpose of this course is to give an introduction to mixed model methods and longitudinal data analysis. Non-linear models and generalized models will also be touched upon briefly. The course aims to enable the participants to formulate a mixed model, define and interpret possible estimators, and implement a mixed model analysis for a two stage nested study, a repeated measures study, and a factorial experimental study. More specifically the participant will be able to

q  Write and interpret mixed models for different study designs

q  Critically evaluate and interpret statistical inference for mixed models and longitudinal data

q  Choose, apply, and interact with statistical software for mixed models.

 

Pre-requisites: Some course in experimental design and familiarity with regression analysis.

Textbook: (Linear mixed models for longitudinal data, Geert Verbeek and Geert Molenberghs Springer Verlag, New York. plus some handouts)

Grading: Home Assignments: 20%, Final written exam: 80%

Exams: More information on the final exam will be a given later.

Software: This course will make extensive use of SAS and, as an alternative, R. Other software such as Splus or SPSS can be used to solve the home assignments but no instructions will be given for these programs.

Computer Assignments: There will be three computer assignment: A Pre-assignment which is an Introduction to SAS/R followed by Assignment 1 and Assignment 2 to perform and hand back Marco Longfils.

Schedule and Tentative Outline of Lectures:

January 25

Introduction (Chap 1-4)

January 27

Estimation for the marginal model (Chap 5)

February 1

Inference for the marginal model (Chap 6)    

Computer Project

February 3

Inference for the random effects ( Chap 8)  

February 8

Software issues (Chap 8 plus lecture notes)   

Computer Project

February 10

Generalized Linear Mixed Models (Handouts)

February 15

Non-linear Mixed Models (Handouts)     

Computer Project  

February 17

Incomplete data (Chap 15-16)

February 22

Imputation in Mixed Models (Handouts)     

Computer Project

February 24

Design and sample size issues (Chap 23)

March 1

Model checking (Handouts)    

Computer Project

March 3

Repetition

 

Reading Instructions

Chapters 1-5

The whole chapter is required

Chapter 6

6.1 to 6.3.3

Chapter 7

7.1-7.7

Chapter 8

The whole chapter is required

Chapter 14

Self-reading

Chapter 15

The whole chapter is required

Chapter 16

The whole chapter is required

Chapter 20

20.3

Chapter 23

The whole chapter is required

Handouts

Generalized mixed models

Handouts

Non-linear mixed models

Handouts

Model checking