I'm running a loglinear multiple regression model on episode level hospital claims data with a random effect for the subject IDs.
My regression formula has the form:
E[ln(Length of Stay)] = Beta* X_matrix + Gamma*(Subject ID)
My code:
proc glimmix data=sasdata.RPT_CTBH15041_CTPAR_CY2015_2017;
class membno provnam grpnum sexcod racecd svccls;
model total_los = provnam grpnum memage sexcod racecd svccls / cl e solution dist=lognormal;
random int / subject=membno;
lsmeans provnam / e cl ilink diff=anom;
ods output ParameterEstimates=ParmEst;
ods output LSMeans=MeanDiff;
run;
Question: Since I specified a loglinear model, not a GLM, does the "ilink" option in the lsmeans statement return the correct parameter estimates and standard errors of the mean LOS (not the median LOS) for the final model?
When you choose DIST=LOGNORMAL, the GLIMMIX procedure models the logarithm of the response variable as a normal random variable, The GLIMMIX Procedure | Model options Because the default link is the identity link, the ILINK option has no effect: the inverse-linked estimates are on the lognormal scale. You have to do the inverse-transformation yourself.
When you choose DIST=LOGNORMAL, the GLIMMIX procedure models the logarithm of the response variable as a normal random variable, The GLIMMIX Procedure | Model options Because the default link is the identity link, the ILINK option has no effect: the inverse-linked estimates are on the lognormal scale. You have to do the inverse-transformation yourself.
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