Picking up from the Proc TTEST step, the code below should combine the means and differences.     proc ttest data =mi_b; var v6wdiff; class group; by _imputation_; ods output statistics=ttest_ds; run; proc sort data=ttest_ds; by class _imputation_; run; proc mianalyze data=ttest_ds; by class; modeleffects mean; stderr stderr; run; ... View more

AFAIK, given the limited information that you provided, the best way to handle this is with PROC QUANTREG, which by default fits the median response. You can define the covariate effect as a polynomial or spline with the EFFECT statement, and test the main effect with nonparametric statistics in the TEST statement. ... View more

It sounds like you should enter the data in terms of some standardized rate, such as Number of defects per 1000 items or Kilograms of waste per 100 workers.   You use the DATA step to convert from raw quantities to rates. You can then use NPAR1WAY or some other procedure to compare the rates. ... View more

@Jep wrote: Appreciate your reply. So I did run a regression and a correlation, but the question now is that, are the results/estimate valid given that one of the variables being compared was used in calculating the other variable being compared with? Since we really know nothing about the problem, and we also don't know what use the correlation/regression will be put to, the answer is: "It depends". ... View more

Although it is possible to fit a quadratic model to 4 time points, so few points provide very little support for the fit. Unless I had a strong theoretical foundation for using the quadratic model, I probably would incorporate time as a classification (discrete) factor.   If the relationship between wt and time is quadratic and if by "slope" you mean the parameter associated with the linear term in the model, then what does slope measure that is of use to you? Looking at only slope in a quadratic model does not make a lot of sense to me, but there may be something about your context that justifies looking at slopes.   If time is incorporated as a classification factor and you want to estimate polynomial trends (including linear), then see SAS Usage Note 22912. The examples use GLM, but the ideas are the same for MIXED. As the Usage Note illustrates, regression is interchangeable with ANOVA with polynomial contrasts.   If you are interested solely in the linear trend (even if the data appear to follow a quadratic trend), then you could just fit a linear model and skip the need for contrast statements.   I hope this helps.   ... View more

Yes, I am using Enterprise Guide. Thanks again for your help. ... View more