RE: algorithm limits

Hi Mark, This is a good question. I am not aware of any public domain simulation work in extreme variability scenarios, so my comments are based on the theory. The fundamental problem with the standard NONMEM algorithm, where the fixed effect and random effects are estimated simultaneously by joint maximum likelihood, is that the size of the variance parameters can bias the mean, sometimes substantially (and hence generalized least squares remains the standard algorithm in the statistical community). If the variance model is even slightly misspecified (which it nearly always is), this can be very damaging to your population mean estimate. Often this leads to overestimates of the mean (so the variance can be smaller) but in some circumstances you can get an excessively high CV% because the mean is underestimated. The other common cause is that you have parameter values close to zero in a subset of subjects, which on a log-scale is minus infinity. Given that you are getting such a high CV% the lognormal may not be the best approach. Switching to additive intersubject variability would remove this dependence between mean and variance, and I would definitely give this a try as an exploratory step. In WinBugs or a nonparametric package, you could explore other distributions - in NONMEM, your only option is subsetting the data manually or using a mixture model, each of which bring new problems. Linearization is a slightly different issue, as this effects how the random effects impact the fit. FOCE linearization will probably give you good individual fits if your individual data contain information about all parameters (ie you could almost get away with a two-stage approach), but this is not the same question as having reliable population parameter estimates. From your description of the model it sounds like you have variability parallel to the time axis, and this is the toughest to linearize - this pushes you away from classic NONMEM as a software choice if the problem lies in a parameter that shifts the predicted curve horizontally in time (like a lag-time does). As a rule of thumb, I would definitely be cynical about a CV over 300%, and would be extremely cautious to use such a model for prediction. My eyebrows start to raise at around 130%. If you decide to simulate, good luck, and I would love to know your findings. Best regards, James G Wright PhD Scientist Wright Dose Ltd Tel: 44 (0) 772 5636914