Log Transformation in NONMEM

Dear all, I developing a mixed effects PK/PD model for VAS sleepiness reported by 20 healthy volunteers after 2 mg oral lorazpam administration. Since the VAS scale is bound, the distribution of VAS scores at various time points is right skewed. However, when I look at the distribution of my WRES in my model it is only very slighlty right skewed. My questions are: 1) Do we base the need to do a Log transformation on diagnostics from the data (e.g. score distributions) or diagnostics of the model (e.g. WRES). 2) Also I have zero baseline data. I realize there is a need to bias the data with a constant if I am to Log transform. I added a small constant (0.01), but in my concordance plots I get all my baseline points below the line of concordance. Is this because the Log transformation treats data between 0 and 1 differently from numbers higher then 1, i.e. (taking the log 0f 0.01 results in a negative number, while doing so for numgers greater then 1 results in positive numbers. Would following the log-transformed model that introduces an additional theta to account for systematic bias be applicable in this scenario (reported by by Beal, JPP 2001;28:481-504)? Has anybody tried this? M = THETA(n) Y = LOG(F+M) + (F/(F+M))*EPS(1) + (M/(F+M))*EPS(2) 3) My maximum VAS is 100 mm (or a log of 2). Yet when I log transform, I get predictions with Log values higher then 2. Is there any way to place a constraint so as not to get predictions higher than log 2. Thanks in advnace, Mohamed Mohamed A. Kamal, Pharm.D. Ph.D. Candidate Department of Pharmaceutical Sciences University of Michigan