I'm building a predictive model that predicts future hospital length of stay. The dataset is episode level, including multiple episodes for many of the members. I'd like to use a penalized GLM (such as a lasso) while at the same time including a member-level random effect.
I know proc glmselect does not have a "random" statement. Is there a feature I'm missing or a workaround that allows random effects to be included in a predictive model?
Answer is no.
But you could use interact term to simulate RANDOM effect.
model y=x1 x2 x1*x2 x1*x1 x2*x2 ;
Answer is no.
But you could use interact term to simulate RANDOM effect.
model y=x1 x2 x1*x2 x1*x1 x2*x2 ;
Thank you Ksharp - I could treat the Member ID # as a fixed effect and model it's interactions, but wouldn't that mean having thousands of Member ID # dummy variables (and using up lots of degrees of freedom)?
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