Linear Mixed Models for Longitudinal Data , 7,5 p (2009)

Linear Mixed Models for Longitudinal Data, Spring 2009



Fridays 9.00-11:00 lectures and Tuesdays 15.00-17.00 computer exercises





Instructors: Ziad Taib ( 0707655471 ) or and Malin Östensson (031 772 53 16)



Linear mixed models for longitudinal data, Geert verbeke and Geert Molenberghs Springer Verlag, New York plus some handouts. The exact chapters that are due can be found here.



The Final written exam will be a closed book exam where the necessary complicated formulas will be provided along with the examination text. No complicated calculations are required but you might need a simple pocket calculator. Both “theoretical” and applied problems can be posed. You should be able to interpret Proc Mixed results but you will not be required to write any code, SAS or otherwise. Out of a total of 30 (100%) possible scores, the computer assignments give a “bonus” of up to 6 scores (20%) while the written exam gives 24 scores (80%). There are three possible grades: Excellent (over 26 scores), Pass (over 16 scores) and do not pass (under 16 scores). Some sample questions covering parts of the course can be found here. Solutions to these as well as examples of “theory” questions can be found in the lecture notes below named Repetition as well as some other sample questions. The solutions to the exam can be found here.


Policy on Homework:

Computer Assignments is to be presented in a report, with programs, output, and statistical notation integrated. The quality of writing and organization, as well as content, will influence the grade. Homework may be turned in directly to Malin Östensson. The deadlines for the assignments are May 11 for lab1 and June 5 for lab2.


Computer Access:

All students are supposed to have a computer account. This account will enable computer lab use and use of computers in the School of Mathematical Sciences.



This course will make extensive use of SAS. This software is available on the computers that will be used. Alternatively, R, Splus or Spss can be used to solve the home assignments but no instructions will be given in these programs.



Schedule And Outline of Lectures:


March 27

Introduction to Linear Mixed Models (Chap 1-4)


Estimation and Inference for the marginal model (Chap 5-6)

April 24

Inference for the random effects, Software issues (Chap 7-8)

May 4

Non-linear and Generalized Linear Mixed Models (Handouts)

May 8

Incomplete data (Chap 15-16)

May 15

Design and sample size issues (Chap 23)


Model checking – Examples - Repetition




Computer assignments:


·         Preassignment: Introduction to SASDataSAS program - zipfile

·         Assignment 1: Lab 1Lab1 - Data1 ZipData1Data2Data 3

·         Assignment 2: Lab2 Data