Hi all, I am planning to do a risk factor analysis using proc mixed (my outcome variable is continues). I have more than 20 variables, and I will screen these variables one by one in a univariable model, and forward the variables with p < 0.15 to the multivariable model and do a backward selection to get the final model. I read some literature that for linear mixed model the assumptions of homoscedasticity and normality of the residuals should be met. My questions are: 1) Should these assumptions be checked every time I run the univariable model, or only check the assumption in the final multivariable model? 2) In case the assumption(s) is not met in the final model, and I need to transform the outcome variable, then should I re-do the screening analysis for all univariable model? 3) Lastly, which results should I present for interpretation, the model with transformed data or I just need to back transform the final model for interpretation? Please enlighten me on these. Thank you! Regards.
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