Using mixed models to analyze Longitudinal intervention

Dear R List,

I am trying to use mixed models to analyze an intervention and want to
make
sure I am doing it correctly.  The intervention is for lowing cholesterol
and there are two groups: one with an intervention and one without.  The
subjects were evaluated a differing amount of time, so there were between
2
and 7 visits, equally spaced.

Sample output is below.  TC is total cholesterol, group.minus.1 is the
intervention indicator and Study.Number is the subject id.  If I'm reading
this correctly, the t-value on group.minus.1 is 0.63 and so the
intervention
did not have a significant effect.  Is that the correct way of
interpreting
this?

Troy

Linear mixed model fit by REML

Formula: TC ~ group.minus.1 + Visit + (Visit | Study.Number) + (1 |
Study.Number)

Data: rawdf

AIC BIC logLik deviance REMLdev

4676 4710 -2330 4670 4660

Random effects:

Groups Name Variance Std.Dev. Corr

Study.Number (Intercept) 857.440 29.2821

Visit 21.471 4.6337 -0.755

Study.Number (Intercept) 508.757 22.5556

Residual 333.710 18.2677

Number of obs: 494, groups: Study.Number, 140

Fixed effects:

Estimate Std. Error t value

(Intercept) 162.3291 4.4392 36.57

group.minus.1 3.4466 5.4699 0.63

Visit -1.4515 0.5997 -2.42

Correlation of Fixed Effects:

(Intr) grp..1

group.mns.1 -0.593

Visit -0.493 -0.043

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