SAS®/GLIMMIX for Mixed Models
and Generalized Linear Mixed Models
May 23-25, 2012
University of Turku, Finland
The Finnish Society of Biostatistics (FSB) is organising a 2-day lecture course + optional 1 day computer workshop on
SAS®/GLIMMIX for Mixed Models and Generalized Linear Mixed Models. There are limited number of seats available in the
workshop and they will be assigned in a first come first serve basis.
The course is given by Prof. Walter W. Stroup, PhD, University of Nebraska, Lincoln.
This course surveys generalized linear mixed model methods and their implementation with SAS®
PROC GLIMMIX. Our focus will be on model construction, estimation and inference in studies
with various forms of clustering, including repeated measures, split-plot and multi-location studies.
Mixed models for normally distributed data will be presented to review important concepts, but our
focus will be on generalized linear mixed model for non-normal data (e.g. proportions and counts)
The course will include use of mixed model methods for planning, design and sample size
determination as well as data analysis. Planning and design are especially important because much
of the conventional wisdom about design follows from normal theory; designs well-suited for
normally distributed data are often poorly suited for non-normal data. All example will use SAS
procedures, mostly PROC GLIMMIX, but some PROC NLMIXED (for certain GLMMs that
cannot be implemented using GLIMMIX) and PROC MIXED and GENMOD (mostly for
comparison with GLIMMIX). Attendees should have background in design and analysis of
The course is supported by the Academic Program of SAS Finland and the
University of Turku.
The first announcement can be found here.
The lectures (23-24.5.2012) will start at 9:00 and end at 17:00. The computer exercises (25.5.2012) will start at 8:00 and end at 16:00.
See venue below for details on the lecture rooms.
Lectures May 23-24, 2012
9:00 - 9:30 Registration (day 1) + coffee/tea
9:30 - 9:40 Opening words, Timo Hurme
9:40 - 10:30 Lecture
10:30 - 10:45 Break
10:45 - 12:00 Lecture
12:00 - 13:15 Lunch
13:15 - 14:30 Lecture
14:30 - 15:00 Coffee/tea
15:00 - 17:00 Lecture
Workshop May 25, 2012
8:00 - 9:30 Workshop
9:30 - 10:00 Coffee/tea
10:00 - 11:30 Workshop
11:30 - 12:30 Lunch
12:30 - 14:00 Workshop
14:00 - 14:30 Coffee/tea
14:30 - 16:00 Workshop
Outline as PDF
I. Essential Elements of a Statistical Model: What a Linear Model Must Do
A. Historical setting
i. “General” linear model once meant
ii. Challenge I: random model effects
iii.Challenge II: distribution of y not Gaussian
iv. Challenge III: correlation among model effects
B. How a model is constructed.
i. Old approach: observation = systematic + random
ii. New: The “probability distribution” form
iii. Essential processes the model accounts for & how this works.
iv. The ANOVA-modeling connection: “What Would Fisher Do?” (WWFD – a process I’ve
developed to aid model construction)
II. Estimation and Inference Essentials
A. Essential inference background
i. Issue unique to generalized linear models: model vs. data scale
ii. Issue unique to models with random model effects: broad vs. narrow inference space
iii. Issue unique to models with “generalized” and “mixed” both present: conditional vs.
B. Estimating equations
ii. Integral approximation: Laplace and Gauss-Hermite quadrature
iii. REML and ML covariance parameter estimation
i. estimable and predictable functions
ii. standard errors and test statistics
iii. degree of freedom and bias correction issues
1. Satterthwaite’s approximation
2. the Kenward Roger correction
3. sandwich (empirical)(robust) estimators
iv. inference for covariance parameters: likelihood ratio tests, fit statistics and interval
III. Power and Sample Size
A. use of GLMM to compare competing designs
i. different design, same sample size often => very different power characteristics
ii. it’s not just how many observations but how wisely you deploy them
B. use of GLMM to assess power for designs intended to be used with non-Gaussian data
i. how it works
ii. examples when primary response variable with be binomial and count
iii. take-home fact: design requirements when the data are non-normal are often very different
from the requirements for normally distributed data
IV. Applications with Specific Types of Response Variables and Distributions
A. Rates and Proportions
i. binary data
ii. binomial data
iii. multinomial data
iv. continuous proportions: the beta distribution
B. Count data
i. Poisson data
ii. potential issues with Poisson data: focus on overdispersion
iii. Negative Binomial data
iv. Too many zeros: Zero-inflated and Hurdle models
C. Time to Event Data
D. What we know and don’t know: Some gray areas and research work in progress
V. Repeated Measures & Spatial Correlation.
A. Review of Mixed Model methods for normally-distributed repeated measures and spatial
correlated error data
B. GLMMs for non-normal repeated measures data –focus on Binomial and Poisson case here
i. “PROC MIXED analog” approach => R-side (GEE is a special case).
ii. “What would Fisher do?” approach => G-side repeated measures model.
C. R- and G-side models are not the same
i. what the differences are
ii. why it matters
D. Choosing a covariance model
E. Choosing the standard error
i. model-based vs. sandwich estimators
ii. bias control
G. Alternative to covariance modeling: radial smoothing
VI. Overdispersion and Temporal & Spatial Correlation Revisited
i. review of basics
ii. the scale parameter and two-parameter non-normal distributions (e.g. negative binomial,
iii. What we do and do not know: future research
B. GLMMs for Temporal and Spatial Correlation
i. review of basics
ii. issues unique to two-parameter non-normal distributions
iii. What we know and do not know: future research
The course dinner is at 19:00 on Wednesday 23.5.2012.
The name of the restaurant will be announced before start of the course.
Please enter your preference (meat/fish/vegetarian) when registering to the course.
The registration deadline is May 11, 2012. To register please go to the
Participant's fees include
- admittance to the course,
- course materials,
- coffee/tee (two coffee breaks/day),
- course dinner (23.5.2012).
NOTE! The early bird registration deadline is April 30, 2012.
||The Finnish Society of Biostatistics (FSB) members**
|On or before April 30, lectures
|On or before April 30, lectures + workshop
|After April 30, lectures
||After April 30, lectures + workshop
** FSB = Suomen biostatistiikan seura
The course will take place at the University of Turku. The seminar and workshop will take place in
the Publicum building (T50 in upper part of the map),
Assistentinkatu 7. The lectures will be held in room PUB1 (1st floor) and the workshop
in room ATK409 (4th floor). Publicum is situated near the other Universities in Turku and Turku University Hospital,
within walking distance from Kupittaa railway station (1.5 km) and about 3 km from the central trainstation and bus stations
For more information about the course, please contact:
- John Aspegrén, sihteeri @ biostatistiikanseura.org
- Timo Hurme, timo.hurme @ utu.fi
Updated: May 9, 2012 | sihteeri @ biostatistiikanseura.org