Statistical Analysis of Repeated Measures Data Using SAS (and R)

Report
Lecture 2
Estimation and Inference
for the marginal model
Ziad Taib
Biostatistics, AZ
April 2011
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Outline of lecture 2
1. A reminder
2. Estimation for the marginal model ML and REML
estimation
3. Inference for the mean structure
4. Inference for the variance components
5. Fitting linear mixed models in SAS
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1. A reminder: The 2-stage Model
Formulation:
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Stage 1
 Response Yij for ith subject, measured at time tij, i = 1, . . . , N, j = 1, . . .
, ni
 • Response vector Yi for ith subject:
Yi  (Yi1 , Yi 2 ,...,Yini )'
Yi  Z i b i   i ,  i ~ N (0, i ), ofteni   2 Ini
Possibly after some convenient transformation
 Zi is a (ni x q) matrix of known covariates and
 bi is a (ni x q) matrix of parameters
 Note that the above model describes the observed variability within
subjects
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Stage 2
 Between-subject variability can now be studied from
relating the parameters bi to known covariates
bi  Ki b  bi
 Ki is a (q x p) matrix of known covariates and
 b is a (p-dimensional vector of unknown regression
parameters
 Finally
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bi ~ N (0, i )
The General Linear Mixed-effects
Model
 The 2-stages of the 2-stage approach can now be
combined into one model:
Average evolution
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Subject specific
The hierarchical versus the marginal
Model
The general mixed model is given by
It can be written as
It is therefore also called a hierarchical model
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Marginally we have that
is distributed as
fYi  y    fYi  y / bi  f bi b db
Hence
EYi   EEYi / bi 
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f(yi I bi)
f(bi)
f(yi)
Example
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The prostate data
A model for the prostate cancer
Stage 1
Yij
 ln(PSAij  1)
 b1i  b 2i tij  b t   ij , j  1,..., ni
2
3i ij
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The prostate data
A model for the prostate cancer
Stage 2
Age could not be matched
 b1i   b1 Agei  b 2Ci  b 3 Bi  b 4 Li  b 5 M i  b1 j 

  
 b 2i    b 6 Agei  b 7Ci  b 8 Bi  b 9 Li  b10 M i  b2 j 
 b   b Age  b C  b B  b L  b M  b 
i
12 i
13 i
14 i
15
i
3j
 3i   11
Ci, Bi, Li, Mi are indicators of the classes: control, BPH, local or
metastatic cancer. Agei is the subject’s age at diagnosis. The
parameters in the first row are the average intercepts for the different
classes.
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The prostate data
This gives the following model
ij
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2. Estimation in the Marginal Model: ML
and REML Estimation
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ML and REML estimates:
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ML and REML estimates (cont’d)
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Estimation based on the marginal model
Vi
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ML estimation




Maximise with respect to b
Replace in the likelihood function
Maximise with respect to a
One can use the EM algorithm or Newton Raphson
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ML estimation
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REML ESTIMATION
 Restricted (or residual, or reduced) maximum likelihood
(REML) approach is a particular form of maximum likelihood
estimation which does not base estimates on a maximum
likelihood fit of all the information, but instead uses a
likelihood function calculated from a transformed set of data,
so that nuisance parameters have no effect.
 In the case of variance component estimation, the likelihood
function is calculated from the probability distribution of a set
of contrasts. In particular, REML is used as a method for
fitting linear mixed models. In contrast to maximum
likelihood estimation, REML can produce unbiased
estimates of variance and covariance parameters.
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Analysis of Contrast Variables
Contrast variables in repeated measures data are linear
combinations of the responses over time for an individual. In
longitudinal studies it is of interest to consider the set of
differences between responses at consecutive time points, that is,
changes from time 1 to time 2, time 2 to time 3, and so forth. A
set of contrast variables can be used to analyze trends over time
and to make comparisons between times. The original repeated
measures data for each individual are transformed into new sets
of variables each given by a set of contrast variables.
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REML estimation
 Given an iid sample Yi i = 1, . . . , N, we can estimate the
variance using
ˆ 2 
N
2
 
1
2
2
ˆ


Y

m
,
E




i
N i 1
 But since m is uknown, we use
2
N
1
ˆ12   Yi  Y  , E ˆ12   2 ,
N i 1
 Based on this we can define
 
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 
N
2
ˆ
s 
1 , E s2   2
N 1
2
REML
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3. Inference for the mean structure
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Approximate Wald tests
 Under the Wald statistical test, named after Abraham
Wald, the maximum likelihood estimate of the parameter(s)
of interest b is compared with the proposed value b0, with
the assumption that the difference between the two will be
approximately normal. Typically the square of the
difference is compared to a chi-squared distribution. In the
univariate case, the Wald statistic is

bˆ  b
 / Var bˆ 
2
0
 which is compared against a chi-square distribution.
Alternatively, the difference can be compared to a normal
distribution. In this case the test statistic is
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bˆ  b / Sebˆ 
0
Approximate Wald tests
H 0 : Lb  0, versus H A : Lb  0
Obs! a is estimated which gives extra variability and bias.
Bias is resolved by using t- or F-test.
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Approximate t-and F- tests
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Robust inference
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Likelihood ratio tests
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Likelihood
ratio tests
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4. Inference for the Variance
Components
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5. Fitting linear mixed models in SAS
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Statistical software
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Software (cont’d)
 SAS – SPSS – BMDP/5v – ML3 – HLM – Splus – R can handle
correlated data but some are more restricted than others.
 Most packages offer a choice between ML and REML and optimisation
is often based on Newton-Raphson, the EM algorithm or Fisher
scoring.
 The user has to specify a model for the mean response that is linear in
the fixed effects and to specify a covariance structure. The user can
select a full parameterisation of the covariance structure (unstructured)
or choose among given covariance structures.
 The covariance structure is also influenced by the inclusion of random
effects and their covariance structure.
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Software (cont’d)
 Output often includes:




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history of optimisation iterations
estimates of fixed effects
covariance parameters with standard errors
estimates of user specified contrasts
Graphics is often limited but can be done in another software
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SAS PROC MIXED and Repeated
Measures
 PROC MIXED of SAS offers greater flexibility for the modelling of
repeated measures data than PROC GLM. (Firstly, the procedure
provides a mechanism for modelling the covariance structure
associated with the repeated measures. Secondly, it can handle some
forms of missing data without discarding an entire subject’s-worth of
data. Thirdly, it has some capability to handle the situation when each
subject may be measured at different times and time intervals.)
 In PROC GLM, repeated measures are handled in a multivariate
framework and it requires a multivariate view of the data. PROC
MIXED, on the other hand, requires a univariate or stacked-data view
of the data. In other words, there is only a single response variable.
The repeated information, including all of the information about the
subjects, is contained in other variables. Proc GLM assumes that the
covariance matrix meets a sphericity assumption compound symmetry.
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SAS PROC MIXED
 Proc mixed was designed to handle mixed models. It has a large

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
choice of covariance structures (unstructured, random effects,
autoregressive, Diggle etc)
PROC MIXED can be used not only to estimate the fixed parameters,
but also the covariance parameters.
By default, PROC MIXED estimates the covariance parameters using
the method of restricted maximum likelihood (REML).
PROC MIXED provides empirical Bayes estimates.
Separate analyses for separate groups can be run using the BY
statement.
Approximate F tests for class variables are obtained using Wald’s test.
All components of the output can be saved as a SAS data set for
further manipulation using other internal (SAS) or external procedures.
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PROC MIXED: Syntax
PROC MIXED < options > ;
BY variables ;
CLASS variables ;
ID variables ;
MODEL dependent = < fixed-effects > < / options > ;
RANDOM random-effects < / options > ;
REPEATED < repeated-effect > < / options > ;
PARMS (value-list) ... < / options > ;
PRIOR < distribution > < / options > ;
CONTRAST 'label' < fixed-effect values ... >
< | random-effect values ... > , ... < / options > ;
ESTIMATE 'label' < fixed-effect values ... >
< | random-effect values ... >< / options > ;
LSMEANS fixed-effects < / options > ;
MAKE 'table' OUT=SAS-data-set ;
WEIGHT variable ;
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In Proc Mixed, the mixed model is specified by means of a
number of statements like CLASS, MODEL, RANDOM and
REPEATED.
 The CLASS statement identifies the classification variables (for
example, gender, person, age, etc.).
 The MODEL statement specifies the model’s fixed effects
equation, Xiβ. Thus, the design matrix Xi is defined and the
model’s intercept is included by default.
 The RANDOM statement is used to specify random effects and the
form of covariance matrix D. (Useful options: SOLUTION: print random effects solution).
 The REPEATED statement models the intra-individual variation
and includes the structure of i=Cov(ei), where i is a block
diagonal matrix for each subject. (If the REPEATED statement is not included it is
assumed that i=σ2I).
 LSMEANS Calculates least squares mean estimates of specified
fixed effects.
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Modelling the Covariance Structure Using the
RANDOM and REPEATED Statements in PROC
MIXED
Measures on different individuals are independent, so covariance needs
attention only with measures on the same individuals. The covariance
structure refers to variances at individual times and to correlation between
measures at different times on the same individual. There are basically two
aspects of the correlation.
 First, two measures on the same individual are correlated simply because they
share common contributions from that individual. This is due to variation
between indivduals.
 Second, measures on the same individual close in time are often more highly
correlated than measures far apart in time. This is covariation within indivduals.
.
Usually, when using PROC MIXED, the variation between indivduals is
specified by the RANDOM statement, and covariation within indivduals is
specified by the REPEATED statement
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 PROC MIXED fits
many different
structures (some
are listed here).
Note also that a
particular
structure may be
fit using more
than one “TYPE”
designation, and
with combinations
of the RANDOM
and REPEATED
statements.
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Data structure of Proc Mixed
 Consider the example where arm strength is measured on 8 patients at
3 different times and where patients have been randomized to one of 2
treatment groups. The multivariate view associated with e.g. PROC
GLM code: would look like below
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 For analysis of this data set using PROC MIXED, the univariate or
stacked-data view will be required. The univariate view below was
obtained by Proc Transpose:
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The rat data
proc mixed data=rat method=reml;
class id group;
model y = t group*t / solution;
random intercept t / type=un subject=id ;
run;
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?
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Results
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Using the option nobound
 Non convergence or non positive definiteness can be
indications of negative variance components. Usually Proc
mixed would not allow that to happen. But using the option
nobound in Proc mixed will result in a new set of estimates
where d22 is negative. Consider the fitted variance function:
Hence, the negative variance component suggests
a negative curvature in the variance function.
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The prostate data
Age could not be matched
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SAS code
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ML and REML estimates:
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ML and REML estimates (cont’d)
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Fitted average profiles
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Mixed Models using R vs. SAS:
Do they give the same answers?
 When you perform a mixed model analysis using R (typically
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because some types of simulations are difficult to obtain in
SAS) the question arises are we using the ”right model”?
The syntax for lme() –is not as transparent as its SAS
counterpart and its documentation is not as good.
Therefore it is interesting to compare the two approaches.
R and SAS give different answers! One of the reasons is
that they apply different restrictions to achieve uniqueness of
estimates.
It is possible to ”force” R to get the same answer as PROC
MIXED in SAS.
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R commands for linear mixed models
 Commands for linear mixed models are in the library
nlme.
data <- read.table(file.choose(), header = T)
attach(data)
Time = factor(Time)
Group = factor(Group)
Subj = factor(Subj)
library(nlme)
model <- lme(y ~ Time + Group + Time*Group, random = ~1 | Subj)
summary(model)
anova(model)
# This model is very close to the one produced by SAS using compound symmetry,
# when it comes to F values, and the log likelihood is the same. But the AIC
# and BIC are quite different. The StDev for the Random Effects are the same
# when squared. The coefficients are different because R uses the first level
# as the base, whereas SAS uses the last.
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?
Any Questions
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