Hmm, that does depend on what your objective is, but I will take a swing at it without knowing what your data is (site means, SDs and Ns, or raw data).
The point I want to address is whether to treat site as a fixed effect (inference is only to the sites included) or a random effect (inference is to all possible sites). All of this assumes using PROC GLIMMIX, as I am fairly certain you are not looking at data with normally distributed residuals if it is primary endpoint clinical data. So, considering SITE as a fixed effect, here is code that will yield a variance estimate for each site:
proc glimmix data=your_data;
class site treatment;
model response = treatment/dist= <insert appropriate distribution here>;
random _residual_/group=site;
run;
For SITE as a random effect, you could try:
proc glimmix data=your_data;
class site treatment;
model response = treatment/dist= <insert appropriate distribution here>;
random intercept/subject=site solution;
run;
Using ODS output statements, you can get the variance estimates for each site from code like this. Weights can then be calculated as 1/variance, merged back against the original dataset, and the data refit using a WEIGHT statement. The Type 3 F test for this analysis reflects any differences in treatment effect after weighting by site.
I am not a clinical biostatistician, but the meta-analyses I have seen generally treat site as a fixed effect. You might want to talk with others about the choice.
SteveDenham
... View more
