Repeated Measures

Repeated Measures
Chapter 16
Repeated Measures
Chapter Table of Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 327
Repeated Measures Analysis . . . . . . . . . . . . . . . . . 329
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348
326 Chapter 16. Repeated Measures
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Chapter 16
Repeated Measures
Introduction
Repeated measures analysis deals with response outcomes measured
on the same experimental unit at different times or under different
conditions. Longitudinal data are a common form of repeated measures in which measurements are recorded on individual subjects
over a period of time. Blood pressure measured once a week for a
month, CD4 counts tracked over a year in an AIDS clinical trial, and
per capita demand deposits over years are examples of longitudinal
data. Repeated measures can also refer to multiple measurements on
an experimental unit, such as the thickness of vertebrae in animals.
Figure 16.1.
Repeated Measures Menu
The experimental units are often subjects. In a repeated measurements analysis, you are usually interested in between-subject and
within-subject effects. Between-subject effects are those whose values change only from subject to subject and remain the same for all
observations on a single subject, for example, treatment and gender.
Within-subject effects are those whose values may differ from mea-
328 Chapter 16. Repeated Measures
surement to measurement, for example, time. Usually, you are also
interested in some between-subject and within-subject interaction,
such as treatment by time.
Since measurements on the same experimental unit are likely to be
correlated, repeated measurements analysis must account for that
correlation. One way of doing this is by modeling the covariance
structure of an individual’s response. The compound symmetry structure assumes the same covariance between any two measurements
and the same variance for each measurement. However, sometimes
the covariance of measures that are close together in time is higher
than the covariance for measurements further apart. In this case,
the first-order autoregressive covariance structure may be more appropriate. Another possible covariance structure is unstructured, in
which you estimate different parameters for the variance of each repeated measurement as well as different covariance parameters for
each pair of repeated measurements.
The Repeated Measures task enables you to specify a repeated measures model with interactions and nested terms, define subject and
repeated effects, and select from a wide range of covariance structures. You can estimate least-squares means for classification effects
and output predicted values and residuals to a data set. Plots include
means plots, predicted plots, and plots of residuals versus within and
between effects. The Repeated Measures task applies methods based
on the mixed model with special parametric structures on the covariance matrices.
The example in this chapter demonstrates how you can use the Repeated Measures task in the Analyst Application to analyze repeated
measurements data.
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Repeated Measures Analysis
The data set analyzed in this task contains data from Littell, Freund,
and Spector (1991). Subjects in the study participated in one of three
different weightlifting programs, and their strength was measured
once every other day for two weeks after they began the program.
The first program increased the number of repetitions as the subject
became stronger (RI), the second program increased the amount of
weight as subjects became stronger (WI), and the subjects in the third
program did not participate in weightlifting (CONT). The objective
of this analysis is to investigate the effect each weightlifting program
has on increasing strength over time. This section also illustrates
how to prepare data in univariate form for this task.
Open the Weightsmult Data Set
The data are provided in the Analyst Sample Library. To open the
Weightsmult data set, follow these steps:
1. Select Tools ! Sample Data : : :
2. Select Weightsmult.
3. Click OK to create the sample data set in your Sasuser directory.
4. Select File ! Open By SAS Name : : :
5. Select Sasuser from the list of Libraries.
6. Select Weightsmult from the list of members.
7. Click OK to bring the Weightsmult data set into the data table.
Data Management
Figure 16.2 displays the Weightsmult data in multivariate form,
which means that a single row in the data table contains all response measurements for a single subject. The Program variable
defines the treatment group and takes the values ‘CONT’, ‘RI’, and
‘WI’. The Subject variable defines each subject, and the variables
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330 Chapter 16. Repeated Measures
s1 through s7 contain strength measurements across time for each
subject.
Figure 16.2.
Weightsmult Data
In order for you to perform the repeated measures analysis using the
Analyst Application, your data must be in univariate form, which
means that each response measurement is contained in a separate
row. If your data are not in univariate form, you must create a new
data table with this structure. This can be accomplished via the Stack
Columns task in the Data menu.
The Stack Columns task creates a new table by stacking specified
columns into a single column. The values in the other columns are
preserved in the new table, and a source column in the new data
set contains the names of the columns in the original data set that
contained the stacked values.
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331
You want to put the values for columns corresponding to the strength
measurement variables s1 through s7 in individual rows, so you
want to stack columns s1,s7. To stack the columns, follow these
steps:
1. Select Data ! Stack Columns : : :
2. Select s1 through s7 and click on the Stack button.
3. Type Strength in the Stacked column: field.
4. Click OK to produce the new data set.
Figure 16.3.
Stack Columns Dialog
The new data set is presented in the project tree under the Stack
Columns folder. The Weightsmult with Stacked Columns folder
contains the new data set with the Strength stacked column, and the
Code node contains the SAS programming statements that generated
the data set.
If a view of the Weightsmult with Stacked Columns data is displayed, close it. Then right-click on the data set node labeled
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332 Chapter 16. Repeated Measures
Weightsmult with Stacked Columns, as displayed in Figure 16.4,
and select Open to bring the new data set into the data table.
Figure 16.4.
Stack Columns: Project Tree
The stacked columns data set contains two new variables. The
Strength variable contains the strength measurements, and the
– Source– variable denotes the measurement times with seven distinct character values: s1, s2, s3, s4, s5, s6, and s7. However, in
this analysis, time needs to be numeric. You can create a numeric
variable called Time by using the Recode Values facility.
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To create the Time variable, follow these steps:
1. Select Edit ! Mode ! Edit.
2. Select Data ! Transform ! Recode Values : : :
3. Select – Source– as the Column to recode.
4. Type Time in the New column name: field.
5. Specify the new column type by selecting Numeric.
6. Click OK to enter values of the Time variable that correspond
to current values of the – Source– variable.
Figure 16.5.
Recode Values Information Dialog
7. Type 1 in the New Value (Numeric) column cell next to s1.
8. Type in the remaining numeric values corresponding to the
original values of the – Source– column. Figure 16.6 displays
the final recoded values.
9. Click OK to create the new variable.
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Figure 16.6.
Recode Values Dialog
The data set now includes a variable Time that contains numeric values for the time of strength measurement. Because the time values
are contained in a new variable, you can delete the original variable from the data set by right-clicking on the – Source– column in
the data table and selecting Delete. Once you have deleted the column, the data set should contain four variables, Subject, Program,
Strength, and Time, as displayed in Figure 16.7.
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Figure 16.7.
335
Weightsuni Data
Before proceeding with the analysis, you can save the new data set
as Weightsuni by following these steps:
1. Select any cell in the data table or reselect the data set node
labeled Weightsmult with Stacked Columns in the project tree.
2. Select File ! Save As By SAS Name : : :
3. Type Weightsuni in the Member Name field and click Save
to save the data set.
Note that the Weightsuni data are in univariate form and should
be the same as the Weights data available in the Analyst Sample
Library.
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Request the Repeated Measures Analysis
To specify the Repeated Measures task, follow these steps:
1. Select Statistics ! ANOVA ! Repeated Measures : : :
2. Select Strength as the dependent variable.
3. Select Subject, Program, and Time as classification variables.
Figure 16.8 displays the dialog with Strength specified as the dependent variable and Subject, Program, and Time specified as classification variables.
Figure 16.8.
Repeated Measures Dialog
Define the Model
To perform a repeated measures analysis, you are required to specify a model, define subjects, specify a repeated effect, and select
one or more structures for modeling the covariance of the repeated
measurements. By defining a factorial structure between Program
and Time, you can analyze the between-subject effect Program, the
within-subject effect Time, and the interaction between Program
and Time.
Each experimental unit, a subject, needs to be uniquely identified in
the Weightsuni data set. The value of the Subject variable for the
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first subject in each separate Program is 1, the value of the Subject
variable for the second subject in each Program is 2, and so on.
Because subjects participating in different programs have the same
value from the Subject variable, you need to nest Subject within
Program to uniquely define each subject.
To define the repeated measures model, follow these steps:
1. Click on the Model button.
2. Select the Subjects tab.
3. Select Subject and click Add.
4. Select Program and click Nest to nest subjects within
weightlifting programs.
Figure 16.9.
Repeated Measures: Model Dialog, Subjects Tab
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5. Select the Model tab.
6. Select Program and Time and click Factorial to specify a
factorial arrangement, which is the main effects for Program
and Time and their interaction.
Figure 16.10.
Repeated Measures: Model Dialog, Model Tab
7. Select the Repeated tab.
8. Select Time and click Add to specify measurement times as
the repeated effect.
This identifies the repeated measurement effect.
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Figure 16.11.
339
Repeated Measures: Model Dialog, Repeated Tab
When analyzing repeated measures data, you must properly model
the covariance structure within subjects to ensure that inferences
about the mean are valid. Using the Repeated Measures task, you
can select from a wide range of covariance types, where the most
common types are compound symmetric, first-order autoregressive,
and unstructured. To select the covariance structure for the analysis,
follow these steps:
1. Select the Covariance Structure tab.
2. Select the Compound symmetry covariance structure.
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340 Chapter 16. Repeated Measures
Figure 16.12.
Repeated Measures: Model Dialog, Covariance
Structure Tab
Close the Model dialog by clicking OK. When you have completed
your selections, click OK in the main dialog to produce your analysis.
Review the Results
The results are presented in the project tree under the Repeated
Measures ANOVA folder, as displayed in Figure 16.13. The nodes
represent the repeated measures results and the SAS programming
statements (labeled Code) that generated the output.
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Figure 16.13.
341
Repeated Measures: Project Tree
You can double-click on the Analysis for Compound Symmetric
Covariances node in the project tree to view the results in a separate
window.
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Figure 16.14.
Repeated Measures: Model Information
Figure 16.14 displays model information including the levels of each
classification variable in the analysis. The Program variable has
three levels while the Time variable has 7 levels. The “Dimensions”
table displays information about the model and matrices used in the
calculations. There are two covariance parameters estimated using
the compound symmetry model: the variance of residual error and
the covariance between two observations on the same subject. The
32 columns of the X matrix correspond to three columns for the Program variable, seven columns for the Time variable, 21 columns for
the Program*Time interaction, and a single column for the intercept. You should always review this information to ensure that the
model has been specified correctly.
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Figure 16.15.
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Repeated Measures: Fitting Information
Figure 16.15 displays fitting information, including the iteration history, covariance parameter estimates, and likelihood statistics. The
“Iteration History” table shows the sequence of evaluations to obtain
the restricted maximum likelihood estimates of the variance components.
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The “Covariance Parameter Estimates” table displays estimates of
the variance component parameters. The covariance between two
measurements on the same subject is 9.6. Based on an estimated
residual variance parameter of 1.2, the overall variance of a measurement is estimated to be 9.6 + 1.2 = 10.8.
Figure 16.16.
Repeated Measures: Tests for Fixed Effects
The “Type 3 Tests of Fixed Effects” table in Figure 16.16 contains
hypothesis tests for the significance of each of the fixed effects, that
is, those effects you specify on the Model tab. Based on a p-value
of 0.0005 for the Program*Time interaction, there is significant evidence of a strong interaction between the weightlifting program and
time of measurement at the = 0:05 level of significance.
Exploring Alternative Covariance Structures
Based on the assumption of the compound symmetry covariance
structure, any two measurements on the same subject have the same
covariance regardless of the length of the time interval between the
measurements. However, repeated measurements are often more
correlated when the measurements are closer in time than when they
are farther apart. In this case, compound symmetry may not be appropriate, and you may want to investigate alternative covariance
structures.
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The first-order autoregressive covariance structure has the property
that observations on the same subject that are closer in time are
more highly correlated than measurements at times that are farther
apart. The first-order autoregressive covariance can be represented
by 2 w , where w indicates the time between two measurements,
stands for the correlation between adjacent observations on the
same subject, and 2 stands for the variance of an observation. For
the first-order autoregressive covariance structure, the correlation between two measurements decreases exponentially as the length of
time between the measurements increases.
To fit an additional repeated measures model with a first-order autoregressive covariance structure, follow these steps:
1. Select Statistics ! ANOVA ! Repeated Measures : : :
Note that the selections for the previous analysis are still specified.
2. Click on the Model button.
3. Select the Covariance Structure tab.
4. Select the 1st-order autoregressive structure.
5. Select Provide information criteria summary to produce
a summary table of model-fit criteria for the two covariance
structures.
6. Click OK in the main dialog to produce your analysis.
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Figure 16.17.
Repeated Measures: Model Dialog, Covariance
Structure tab
Although this analysis models only two different covariance structures, the Analyst Application provides a wide range of structures
to choose from, including unstructured, Huynh-Feldt, Toeplitz, and
variance components. To select other structures, click on the down
arrow next to an Other check box and choose from the resulting
drop-down list.
Double-click on the Analysis for First Order Autoregressive Covariances node in the project tree to view the results in a separate
window.
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Figure 16.18.
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Repeated Measures: Test for Fixed Effects for Autoregressive Covariance
Figure 16.18 displays the Type 3 tests for fixed effects based on the
first-order autoregressive covariance model. Note that with a p-value
greater than 0.30, the Program*Time interaction is not significant at
the = 0:05 level of significance. The p-value is different from the
p-value of the same test based on the compound symmetry covariance structure, and the two models lead to different conclusions. You
can assess the model fit based on different covariance structures by
comparing criteria that is provided in the Information Criteria Summary window in Figure 16.19.
Figure 16.19.
Repeated Measures: Information Criteria Summary
The process of selecting the most appropriate covariance structure
can be aided by comparing the Akaike’s Information Criteria (AIC)
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and Schwarz’s Bayesian Criterion (SBC) for each model. When you
compare models with the same fixed effects but different variance
structures, the models with the highest AIC and SBC are deemed
the best. In this example, the autoregressive model has higher values for both AIC and SBC, showing considerable improvement over
the model with a compound symmetry structure. Based on the information criteria as well as the intuitively sensible property of the
correlations being larger for nearby times than for far-apart times,
the first-order autoregressive is the more suitable fit for this model.
References
Littell, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D.
(1996), SAS System for Mixed Models, Cary, NC: SAS Institute
Inc.
Littell, R. C., Freund, R. J., and Spector, P. C. (1991), SAS System
for Linear Models, Third Edition, Cary, NC: SAS Institute Inc.
SAS Institute Inc. (1999), SAS/STAT User’s Guide, Version 7-1,
Cary, NC: SAS Institute Inc.
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The Analyst Application, First Edition, Cary, NC: SAS Institute Inc., 1999. 476 pp.
The Analyst Application, First Edition
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