Linear Mixed Models for Longitudinal Data, 7.5 scores, spring 2017
Lectures:
§ Alexandra Jauhiainen
§ 725518416
§ Alexandra.jauhiainen@astrazeneca.com
§ Ziad Taib
§ 0707655471
Computer projects:
§ Trasieva, Teodora
§ teodora.sheytanova@astrazeneca.com
§ 0317761000
§ Marco Longfils
§ 0317723574
§ longfils@chalmers.se
Schedule:
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 |