Course syllabus and information sheet
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 1: Introduction
Overheads: pdf file
Homework: Problem Set 1
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 2: ANOVA approaches to longitudinal data
Overheads: pdf file
Example 2a: Analysis of vocabulary data from Bock
(1975) using univariate repeated measures ANOVA (SAS code and output)
Example 2b: IML code for generating orthogonal polynomial contrast matrix (SAS code and output)
Plots: Plots of vocabulary data including matrix, spaghetti and box
plots. (SAS
code, and matrix plot,
spaghetti
plot, boxplot)
Example 2c: Analysis of Pothoff & Roy data using univariate repeated measures ANOVA model with two groups (boys and girls). Includes a plot of the means across time for the two groups. (SAS code and output)
Example 2d: Analysis of Pothoff & Roy data using univariate repeated measures ANOVA. This analysis is for a main effects model - no group by time interaction. (SAS code and output)
Example 2e: Test of sphericity for the Pothoff & Roy data (SAS code and output)
Plots: Plots of Potthoff & Roy data including matrix, spaghetti, mean, and box plots (most by gender). (SAS code and plots)
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 3: MANOVA approaches to longitudinal data
Overheads: pdf file
Example 3a: Analysis of vocabulary data using MANOVA (SAS code and output)
Example 3b: Analysis of sleep data using IML to do MANOVA (SAS code and output) ***OPTIONAL***
Example 3c: Analysis of Prozac weight data using MANOVA. Includes use of CONTRAST statement to obtain estimates of a-priori contrasts for the multi-group situation. (SAS code and output)
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 4: Mixed-effects regression models (MRM) for continuous outcomes
Overheads: pdf file
Homework: Problem Set 2
Example 4a: Analysis of Riesby dataset using MRM. This example has a few different PROC MIXED specifications, and includes a grouping variable and curvilinear effect of time. (SAS code and output)
Dataset: Riesby dataset - the variable order and names are indicated in the previous handout.
Plots: Plots of Riesby dataset including matrix, box, spaghetti, linear, quadratic, and individual plots. (SAS code and plots)
Week 4: Thursday September 15, 2011
Example 4b: Analysis of Riesby dataset. This handout shows how empirical Bayes estimates can be output to a dataset in order to calculate estimated individual scores at all timepoints. (SAS code and output)
Plots: Plots of Riesby dataset including mean and box plots by group. (SAS code and plots)
Example 4c: Analysis of Riesby dataset. This handout has the analysis considering the time-varying drug plasma levels, separating the within-subjects from the between-subjects effects for these time-varying covariates. (SAS code and output)
Dataset: Riesby dataset with time-varying covariates - the variable order and names are indicated in the above handouts.
Plots: Plots of time-varying drug plasma levels including matrix and box plots. (SAS code and plots)
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 5: Mixed-effects polynomial regression models for continuous
outcomes
Overheads: pdf file
Example 5a:
Analysis of Riesby dataset. This handout contrasts
quadratic trend models using the raw metric of week versus orthogonal
polynomials. (SAS code and output)
Example 5b: Converting data from univariate to multivariate format, and getting the observed correlations and variance-covariance matrix of the repeated measures (SAS code and output)
Example 5c: IML code that illustrates the calculation of estimated means and the variance-covariance matrix (SAS code and output)
Homework: Problem Set 3
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Section 4.6 – Estimation of MRMs for continuous outcomes
Overheads: pdf
file
Continuation of material on estimation.
Analysis of Riesby dataset. This handout includes SAS IML code for a random-intercepts model. A comparison to analysis using PROC MIXED is also included. (SAS code and output) ***OPTIONAL***
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 6: Covariance pattern models for continuous outcomes
Overheads: pdf file
Continue with material from Chapter
6
Homework: Problem Set 4
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 7: Mixed regression models with autocorrelated
errors
Overheads: pdf file
Example 7.1: SAS code - SAS code for analysis of Bock dataset. This handout lists syntax for several PROC MIXED analyses including (a) mixed-effects models, (b) covariance pattern models, and (c) mixed-effects models with autocorrelated errors.
Example 7.2: SAS code and output - SAS IML code and output from analysis of Bock dataset. This handout uses IML to provide estimated means, variances, and correlations across time based on mixed-effects models with autocorrelated errors.
Dataset: Bock dataset - the variable order and names are indicated in the previous two handouts.
PRACTICAL ISSUES: pdf file
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 9: Mixed-effects regression models for binary outcomes.
Overheads: pdf file
Example 9.1:
Analysis of the NIMH Schizophrenia dataset.
This handout provides SAS (PROC LOGISTIC, NLMIXED) code for running
ordinary logistic regression and mixed-effects logistic regression. (SAS code)
Example 9.2: This handout shows how to
use PROC IML to get the marginalized probability estimates from the above
NLMIXED analysis for the random intercept model (SAS code)
Dataset: SCHIZ dataset - the variable order and names are indicated in
Example 9.1.
Homework: Problem Set 5, SAS code, Dataset
Week 10: Tuesday October 25,
2011
Example 9.3: This handout shows how
to use PROC IML to get the marginalized probability estimates from the NLMIXED
analysis for the random trend model of the NIMH data (SAS code)
Example 9.4:
GEE analysis of the NIMH Schizophrenia dataset using SAS PROC GENMOD (SAS code)
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Section 9.6 – Estimation of mixed-effects binary regression models
Overheads: pdf file
Example: SAS IML macro program for random-intercepts binary regression (SAS code) *** OPTIONAL ***
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 8: Generalized Estimating Equations (GEE) Models.
Overheads: pdf file
Example 8.1: Using the NIMH Schizophrenia dataset, this handout has PROC GENMOD code and output from several GEE analyses varying the working correlation structure. (SAS code and output)
Example 8.2: PROC GENMOD code and output from analysis of Robin Mermelstein's smoking cessation study dataset. This handout illustrates GEE modeling of a dichotomous outcome. Includes CONTRAST statements to perform linear combination and multi-parameter Wald tests, and OBSTATS to yield estimated probabilities for each observation. (SAS code and output)
Dataset: Smoking data - the variable order and names are indicated in the above handouts.
Homework: Problem Set 6
Continue with
GEE material – GEE compared with MRM
Example 8.3: PROC IML code and output
showing how to get the marginalized probability estimates from GEE and NLMIXED
analysis for a random-intercepts model, including using quadrature
for the latter (SAS code and
output)
Reading material: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"
Chapters 10 & 11: Mixed-effects regression models for ordinal and nominal
outcomes.
Overheads: pdf file
Examples using SAS:
schzonl.sas - SAS code for
mixed-effects proportional odds regression analysis of NIMH Schizophrenia
data. schizx1.dat - ASCII datafile.
schzofit.sas - SAS IML code for
obtaining marginal probability estimates based on mixed-effects proportional
odds regression analysis of NIMH Schizophrenia data.
sandonl.sas - SAS code for mixed-effects proportional odds and non-proportional odds analyses of San Diego homelessness data. sdhouse.dat - ASCII datafile.
sandofit.sas - SAS code for
obtaining marginal probability estimates based on mixed-effects proportional
odds and non-proportional odds analyses of San Diego homelessness data.
sandnnl.sas - SAS code for mixed-effects multinomial analyses of San Diego homelessness data.
sandnfit.sas
- SAS code for obtaining marginal probability estimates based on mixed-effects
nominal model analyses of San Diego homelessness data.
Continue with material from
Chapters 10 & 11
Reading material: Hedeker, D. and
Gibbons, R.D. "Longitudinal Data Analysis"
Chapter 14: Missing Data in Longitudinal Studies.
Overheads: pdf file
Example 14.1: Analysis
of NIMH Schizophrenia dataset for time to dropout using discrete-time survival
analysis. Shows how to
create the person-period dataset.
(SAS code)
Reading material: Hedeker, D., & Gibbons, R.D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64-78. (pdf file)
Reading material: Hedeker,
D., Gibbons, R.D., & Waternaux, C. (1999). Sample size estimation for longitudinal designs with
attrition: comparing time-related contrasts between two groups. Journal
of Educational and Behavioral Statistics, 24:70-93. (pdf file)
Overheads: (pdf file)
RMASS2.EXE contains: executable program for sample size determination
based on this paper.
RMASS2.PDF contains: PDF version of program guide
If time permits:
Reading
material: Hedeker, D., Mermelstein, R.J., & Demirtas, H. (2008). Analysis of Binary Outcomes with
Missing Data: Missing=Smoking, Last Observation Carried Forward, and a Little
Multiple Imputation. Addiction, 102:1564-1573. (pdf
file) (SAS code)
(SAS code description)
(data)
Overheads: (pdf file)
Reading material: Hedeker,
D., Mermelstein, R.J., & Demirtas, H. (2008). An application of a mixed-effects location scale model for analysis
of Ecological Momentary Assessment (EMA) data. Biometrics, 64:627-634. (pdf
file) (SAS code)
Overheads: (pdf file)
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extras:
bockgee - SAS PROC MIXED & GENMOD code and output from analysis of Bock dataset. This handout compares results from mixed-effects modeling to GEE modeling for this dataset with no missing data across time and a continuous outcome variable.
riesgee2 - SAS PROC
MIXED & GENMOD code and output from analysis of Riesby
dataset. This handout compares results from mixed-effects modeling to GEE modeling
for this dataset which does have missing data across time and a continuous
outcome variable.
The following datasets are in
ASCII form and can be downloaded
vt.dat has data on 22 subjects from a study of affective facial
expressions (Vasey and Thayer, 1987).
In this study several pieces of music were played to each subject in an attempt
to elicit selected affected states. Trial 1 was a baseline, relaxing
music condition. Trial 2 was designed to produce positive effects, and
trial 3 was designed to produce agitation. Each trial lasted 90 seconds
and the response variable at each trial was the mean electromyographic
(EMG) amplitude from the left brow region. The variable order in the datafile is subject number followed by the three trial EMG
measurements. For problem set 1.
box.dat has body weight data for 30 rats measured at baseline and at weekly intervals for 4 weeks. There are three groups (1=control, 2=thiouracil, 3=thyroxin) of 10 rats each.. The datafile contains, in order, GROUP, RAT, BW0, GAIN1 (BW1-BW0), GAIN2 (BW2-BW1), GAIN3 (BW3-BW2), and GAIN4 (BW4-BW3). For problem set 1.
SCHIZREP.DAT has severity of illness scores on 437 schizophrenics measured across time. Subjects were randomized to one of four treatments: placebo, chlorpromazine, fluphenazine, or thioridazine. Here the drug groups have been combined into one group. The datafile contains, in order, Patient ID, IMPS79 (7-point severity scale), week (week 0 to week 6, though most of the measurement occured on weeks 0, 1, 3, & 6), treatment group (0=placebo, 1=drug), and sex (0=female, 1=male). For problem set 2.
SCHIZX1.DAT - Same dataset as above with the following variables, in order, Patient ID, IMPS79 (7-point severity scale), IMPS79b (binary version of IMPS79), IMPS79o (ordinal version of IMPS79), intercept (a column of ones), treatment group (0=placebo, 1=drug), week (week 0 to week 6, though most of the measurement occured on weeks 0, 1, 3, & 6), square root of week (helps to linearize the relationship of IMPS79 over time), and treatment group by square root of week (interaction term).
RIESBYT4.DAT has
RIESORD3.RRM has
Any questions or comments to Don Hedeker email: hedeker@uic.edu