I'll disagree here. I think your results are very consistent. Your 3-way
interaction may be driven more by errors and noise than by real effects.
Everything else looks surprisingly close. However, an artificial
than-0.05 call will point out what look to me to be really minor differences
in places. If you call p-values of .053 and .036 'different', then you are
making an arbitrary judgment.
 Is there a problem with the model specifications? I can't tell. You
complex model, with no residual diagnostics and no residual plots and no
of the underlying assumptions. In the PROC MIXED formulation, you are
a simple autoregressive time series structure. In the PROC GENMOD
you are using GEE with an analogous covariance structure. Perhaps neither
reaosnable, given the data. I *do* suspect that your DIST=NORMAL option
is not correct. Is Y *really* continuous, or does it just have a number of
possible values? You have stated that it is bound between 0 and 7, so it
meet the specs I would expect to see in a real normally distributed
 Are the two methods not equivalent? They are not. See my above
discussion. And be apprised that the underlying methods used to fit the
are not exactly the same, either.
 If they are, which result is more valid?? I'm going to guess here that
NEITHER is valid. I'm going to guess that the normal errors are not
I'm going to guess that the AR(1) model may not be reasonable. I'm going
to guess that when you do your diagnostics, you'll find at least one outlier
or leverage point which could distort your results.
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