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
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.
Thanks a lot in advance.