lme to determine if there is a group effect

I apologize for sending this message again. The last time I sent it, the
subject line was not correct. I have corrected the subject line.

I am trying to run a repeated measures analysis of data in which each
subject (identified by SS) has 3 observations at three different times (0,
3, and 6). There are two groups of subjects (identified by group). I want
to know if the response differs in the two groups. I have tried to used
lme. Lme tell me if there is a time effect, but does not tell me if there
is a group effect. Once I get this to work I will want to know if there is
a significant group*time effect. Can someone tell me how to get an estimate
for group. Once I get that, I believe getting an estimate for group*time
should be straight forward. The code I have tired to use follows.
Thank you,
John

# This is my data
data1

   SS group time     value baseline
1   1  Cont    0  9.000000 9.000000
2   2  Cont    0  3.000000 3.000000
3   3  Cont    0  8.000000 8.000000
4   4  Inte    0  5.690702 5.690702
5   5  Inte    0  7.409493 7.409493
6   6  Inte    0  7.428018 7.428018
7   1  Cont    3 13.713148 9.000000
8   2  Cont    3  9.841107 3.000000
9   3  Cont    3 12.843236 8.000000
10  4  Inte    3  9.300899 5.690702
11  5  Inte    3 10.936389 7.409493
12  6  Inte    3 12.358499 7.428018
13  1  Cont    6 18.952390 9.000000
14  2  Cont    6 15.091527 3.000000
15  3  Cont    6 17.578812 8.000000
16  4  Inte    6 12.325499 5.690702
17  5  Inte    6 15.486513 7.409493
18  6  Inte    6 18.284965 7.428018

# Create a grouped data object. SS identifies each subject
# group indentifies group, intervention or control.
GD<- groupedData(value~time|SS/group,data=data1,FUN=mean)
# Fit the model.
fit1 <- lme(GD)
cat("The results give information about time, but does not say if the

gruops are different\n")
The results give information about time, but does not say if the gruops
are different

summary(fit1)

Linear mixed-effects model fit by REML
 Data: GD
       AIC      BIC    logLik
  74.59447 81.54777 -28.29724

Random effects:
 Formula: ~time | SS
 Structure: General positive-definite
            StdDev    Corr
(Intercept) 1.3875111 (Intr)
time        0.2208046 -0.243

 Formula: ~time | group %in% SS
 Structure: General positive-definite
            StdDev    Corr
(Intercept) 1.3875115 (Intr)
time        0.2208051 -0.243
Residual    0.3800788

Fixed effects: value ~ time
               Value Std.Error DF   t-value p-value
(Intercept) 6.747442 0.8135067 11  8.294268       0
time        1.588653 0.1326242 11 11.978601       0
 Correlation:
     (Intr)
time -0.268

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max
-1.11412947 -0.44986535  0.08034174  0.34615610  1.29943887

Number of Observations: 18
Number of Groups:
           SS group %in% SS
            6             6

John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and
Geriatric Medicine
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)

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