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)
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)
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.
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)
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.
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 27, 2009
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
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 and SAS
output)
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) (appendix)
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.
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