Dear all, a question that I find difficult to answer: when I apply the proc glimmix on both longitudinal and non-longitudinal data and I notice that normality/homoscedasticity is violated I use the possibility of choosing the connection function "dist" but the problem I am noticing is that as long as dist = Gaussian I have no problems transcribing the estimates since they are those of the starting data. The problem arises when I choose another distribution, for example the lognormal or gamma or other function, because now the estimates are calculated by referring to that distribution, if I reported the estimates in a table it would be complicated to explain them because they are totally different from the starting and real ones and being very different from reality how can I manage to solve this problem? Is there a counter-function or command to return to the original data? Thank you very much Giuseppe   Example (with this procedure the hypotheses are verified): ods graphics on; proc glimmix data=x.y plots=(studentpanel residualpanel); class id time therapy;  model z =time|therapy/ solution dist=lognormal cl;  random intercept / subject=id type=un; lsmeans time|therapy / pdiff=all adjust=tukey cl; run; ods graphics off;   The estimates I get are: Control a T0 = 10.24 Tratment a T0 = 9.97 Control a T90 = 9.88 Treatment a T90 = 8.60   Same example as above (with this procedure the assumptions are violated): ods graphics on; proc glimmix data=x.y plots=(studentpanel residualpanel); class id time therapy;  model z =time|therapy/ solution dist=gaussian cl;  random intercept / subject=id type=un; lsmeans time|therapy / pdiff=all adjust=tukey cl; run; ods graphics off;   The estimates I get are: Le stime che ottengo sono: Control a T0 = 122598 Tratment a T0 = 40377 Control a T90 = 52574 Treatment a T90 = 9508.98   As you can see they are very different. So if I reported those obtained with the lognormal how could I explain those numbers that are very far from the clinical reality? I can't find a way to go back to the original data but with the p of the lognormal procedure. ... View more

Dear All,   in SAS by default there is the Mann-Whitney test which has disadvantages that can be solved with the Brunner-Munzel test which unfortunately is not implemented in SAS. Could any of the developers generate an ad-hoc program? There is the basic version and the version with permutations. In R there are different versions therefore it would be important to have these two versions also in SAS to enhance the routines of this program and also innovate the tests compared to the now old ones. I enclose some articles and two of them contain the program developed in R.   Thank you Giuseppe ... View more

Dear all,   these days I was reading an article in which the authors, first name Marcel A.L.M. van Assen, create with the R software a very interesting graph, meta-plot, to be used when doing meta-analysis. Could this graph be made with SAS? I write here the link https://osf.io/eayfr/ where to find the R code and all the material used by the authors. The title of the paper is: The Meta-Plot A graphical tool for interpreting the results of a meta-analysis.   Thank you very much   Giuseppe ... View more