You wrote: As the response was not normally distributed, I should/counld not use multiple linear regression This is not correct. Multiple regression makes no assumptions about the distribution of the response. It assumes that the errors are normally distributed. You can't see the errors, but you can see the residuals and the output from e.g. PROC GLM provides ways to assess the normality of the residuals. You also wrote i recoded the negative scores in the samples (only about 7% of the patients) so that I can use some GLMs. This is surely not a good thing to do. We don't change the data to make it fit our model without very good reason. It's also not necessary here. We don't currently have non-parametric multivariate models to use. Since you have SAS, yes, you do have these. You can try either ROBUSTREG or QUANTREG. See my paper "Should more of your PROC REGs be QUANTREGs and ROBUSTREGs" Finally, as to your confusion regarding the different results from the different methods - it's not really surprising. Each of the methods asks a different question so each gives a different answer.
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