# interpretation of treatment effect from linear mix model when Y is log-transformed in longitudinal analysis

In a clinical study, 100 patients are evenly divided into two treatment group, trtA and trtB. For each patient, a biomarker is measured at 5 different visit timepoints. Y is the measured biomarker level.

I'm using linear mix model to evaluate treatment effect along the time course. I can build two models with Y or log(Y) as below.

# model1
Y = lmer(Y~Visit+treatment+(1|subject)), data=data )
# model2 (log-transformed Y)
log(Y) = lmer(Y~Visit+treatment+(1|subject), data=data)


My question is: say treatment effect is significant, how should I interpret model result (significance of treatment effect), using original Y vs. log-transformed Y? (model1 vs model2). I feel somehow they should be different....

There is a statistically significant difference between
trtA and trtB along the time course...?


One additional question: How should I interpret model result, when Visit is treated as factor variable vs continuous variable (number of days), say the treatment term is significant.