I am analyzing data from a multi-location trial (5 locations) to test the effectiveness of a treatment with 2 levels.
The design is RCB with 3-4 replications in every location.

I use the model below:

proc mixed data=mydata;
class location rep trt ;
model Y=trt/ddfm=kr2 residual;
random location rep(location ) trt(location );
lsmeans trt/pdiff;
estimate "Improved vs Control at loc1" trt -1 1 | trt(location ) -1 1 0 0 0 0 0 0 0 0;
estimate "Improved vs Control at loc2" trt -1 1 | trt(location ) 0 0 -1 1 0 0 0 0 0 0 ;
estimate "Improved vs Control at loc3" trt -1 1 | trt(location ) 0 0 0 0 -1 1 0 0 0 0 ;
estimate "Improved vs Control at loc4" trt -1 1 | trt(location ) 0 0 0 0 0 0 -1 1 0 0 ;
estimate "Improved vs Control at loc5" trt -1 1 | trt(location ) 0 0 0 0 0 0 0 0 -1 1 ;
estimate "Improved vs Control across locations" trt -5 5 | trt(location ) -1 1 -1 1 -1 1 -1 1 -1 1/divisor=5 ;

I am interested in the trt effect across locations (that is why I used the random effects in the random statement).
The last estimate statement was used as a test to see if I would get exactly the same results with the LSMEANS.
But although the estimates are the same, the standard errors are different.

I thought they should be the same. Why the standard errors (and p-values) are different?
Does LSMEANS and ESTIMATE with random effect test something different?

Since I am interested in the trt difference across the locations (locations as random effect), which result should I choose?

Thank you
Thank you hlsmith.
I will read it thoroughly because I really need to understand the difference.
It appears that LSMEAN compute the treatment effect across locations differently than the ESTIMATE (BLUP). I don't think I am doing something wrong, I follow the examples in chapter 6 in SAS for Mixed Models, second edition from Littel et al.

Do LSMEANS account for random effects the same way as the ESTIMATE? If not, how should I interpret the results?

I need to understand how they differ and which one is the correct result for narrow and broad inference in a mixed model.

I need to know why they are different. If the above BLUP estimate is the inference across the 5 locations, what is the LSMEANS then?
Don't LSMEANS take into account the random effects and produce estimate of fixed effects across the 5 locations? That is what I thought.
So which one should I report?

There has to be an explanation (or me making a mistake in the model), it is just not obvious to me.


Not a robit
Well if you are getting the same estimate but the SEs are different, they are just calculating them differently - the SEs. So they are estimating the same effect but are using a different formula for SEs. I see you posted this on SAS Communities - if they can't answer it who can? Well if you give them a bump and reasonable time and you don't get a resolve, I would email the author of the book you referenced or just give SAS a call. I have never done that, but I know people do call them with analytic questions. I am still curious how different the SE's are? is it a trivial amount?
Thank you hlsmith for your thoughts and interest on this. I will try to email the author of the book, there is no reply to my post on SAS Communities.

The std error for LSMEANS is 1.01 (p-value=0.0197) and for the BLUP is 0.83 (p-value=0.0005). The issue is that in another dataset, the difference might be around the 5% significance level and will make it difficult to be sure what to report.

I believe that the estimate statement should give exactly the same result with the LSMEANS. Maybe that is what I am doing wrong, not specifying the estimate statement correctly.