Thanks for your effort, Paige! ... View more

So which sentence are you referring to? And if there is no transformation to turn my data into something with close to normal errors, aren’t we eventually back at my initial question if there is any non-parametric alternative? ... View more

Hey ksharp, Thanks for helping me! Already thought about that, but hoped that there is a more elegant way. Best Moritz ... View more

Its not that I haven't tried box-cox yet, but I like the concept of using box-cox to get an idea of the next best distribution. After using the suggested code as displayed:    proc sql noprint; select 1-min(bili) into :c trimmed from my_data; quit; %put &=c; proc transreg data=my_data ss2 details plots=(boxcox); model BoxCox(bili / parameter=&c geometricmean convenient lambda=-2 to 2 by 0.05) = identity( group | Endpoint); output out=TransOut residual; run; proc univariate data=TransOut(keep=Rbili); histogram Rbili / normal kernel; qqplot Rbili / normal(mu=est sigma=est) grid; ods select histogram qqplot GoodnessOfFit Moments; run; I got the following results which still do not look 'normal enough' do they?   Just to get this right, if the box-cox CONVIENIENT option would have suggested for instance a Lamda=0 and the residuals would have been approximately normal, lets say everything p>0.01, I could have used the parametric approach with dist=logN?   ... View more

Hey Paige, thanks again for helping me to sort this out.    As stated in the article you need to check the residuals for approximate normality. And that is what I did. Please forgive me the sloppy simplification to state that my data are not gaussian. But to demonstrate the non-normality of the residuals I provided a residual normal quantile plot. To my mind it looks like a systematic deviation and not a random scatter, you will find the tests for normality of the residuals attached.   Of course treatment was included into the model in terms of the group variable (the groups are 'control' and 'treatment').   best Moritz ... View more

Dear Paige,    thank you very much for your reply!   Bilirubin was measured in blood-samples at the respective endpoints and should be a continuous response variable and not a predictor variable, it just shows a rather dichotomous distribution, because the values are either very high in response to the treatment or very low in the control group. And the distribution of the residuals of the response variable must be specified, doesn't it? Maybe my explanation above was not specific enough or I misunderstood something.   I measured some other values, for instance cytokines that showed a logN distribution as expected. Those were analyzed using PROC GLIMMIX as shown below. I am looking for an alternativ if the distribution is unknown.    best Moritz   proc glimmix data=my_data; Class group Endpoint; Model measured_value = Group Endpoint Group*Endpoint /dist=logN ddfm=kr2; Random _resid_ /group=Group; covtest homogeneity; output out=resids resid=r; LSMeans Group*Endpoint/ slicediff=Endpoint adjust=sim stepdown(type=logical) adjdfe=row lines plot=meanplot(sliceby=group join cl); run; Proc univariate data=resids normaltest; var r; qqplot; run;   ... View more