**BSTT537 Longitudinal Data Analysis - Fall 2013**** **

**Instructor: Don Hedeker**** **

**Reading Materials,
Overheads, Examples, and Problem sets*** *

**Week 1: Tuesday
August 27, 2013**** **

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

**Week 1: Thursday
August 29, 2013**

*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)

**Week 2: Tuesday
September 3, 2013**** **

*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)

**Week 2: Thursday
September 5, 2013**

*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)

**Week 3: Tuesday
September 10, 2013**** **

*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)

**Week 3: Thursday
September 12, 2013**

*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

**Week 4: Tuesday
September 17, 2013**** **

*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 19, 2013**** **

*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)

**Week 5: Tuesday
September 24, 2013**

*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)

**Week 5: Thursday
September 26, 2013**

*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

**Week 6: Tuesday
October 1, 2013**** **

*Reading material*: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"

Section 4.6: Estimation of MRMs for continuous outcomes

*Overheads*: pdf
file

**Week 6: Thursday
October 3, 2013 **

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***

**Week 7: Tuesday
October 8, 2013**** **

*Reading material*: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"

Chapter 6: Covariance pattern models for continuous outcomes

*Overheads*: pdf file

**Week 7: Thursday
October 10, 2013**

Continue with
material from Chapter 6

*Homework*: Problem Set 4

**Week 8: Tuesday
October 15, 2013**** **

*Reading material*: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"

Chapter 7: Mixed regression models with autocorrelated
errors

*Overheads*: pdf file

**Week 8: Thursday
October 17, 2013**** **

*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

**Week 9: Tuesday
October 22, 2013**** **

*Reading material*: Hedeker,
D. and Gibbons, R.D. "Longitudinal Data Analysis"

Chapter 9: Mixed-effects regression models for binary outcomes.

*Overheads*: pdf file

**Week 9: Thursday
October 24, 2013**

*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 29, 2013 **

*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)

**Week 10: Thursday
October 31, 2013**

*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 ***

**Week 11: Tuesday
November 5, 2013**

*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)

**Week 11: Thursday
November 7, 2013**** **

*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

**Week 12: Tuesday
November 12, 2013**** **

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)

**Week 12: Thursday
November 14, 2013**

*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.

**Week 13: Tuesday
November 19, 2013**** **

Continue with material from Chapters 10 & 11

**Week 13: Thursday
November 21, 2013**

*Reading material*: **H**edeker, 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)

**Week 14: Tuesday November 26, 2013 **

*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

**Week 14: Thursday November 28, 2013 - NO
CLASS - Thanksgiving Day**

**Week 15: Tuesday
December 3, 2013**** **

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

**Week 15: Thursday
December 5, 2013**** **

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)*

-----------------------------------------------------------------------------------------------------------------------------

*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**