Re: Rational of using IOV

Nicolas, Within subject variability in a parameter (e.g. clearance) can be predictable ('fixed effect') e.g. associated with maturation of renal function in young children, or can be unpredictable ('random effect') i.e. apparently varying randomly from occasion to occasion. IOV is usually used to describe the random between occasion variability. Because this is by assumption a random source of variability it makes no difference which occasion (or treatment) you associate it with. In your case you have 3 occasions for each patient. Just number the occasion 1,2 and 3 for every patient and estimate IOV in the usual way as a random effect with variance described by OMEGA. I assume you use the word 'cure' to mean treatment rather than complete remission of the disease. If you think there may be non-random differences between occasions e.g. due to different treatments on each occasion, then you can model these with a predictable ('fixed effect') treatment covariate model with each treatment type representing a different value of the treatment covariate. If you only have a few patients for a particular treatment type then of course you are unlikely to detect a treatment type effect and should be very cautious about accepting any effect you find as being real (Ribbing & Johnson 2004). Best wishes, Nick Ribbing J, Jonsson EN. Power, Selection Bias and Predictive Performance of the Population Pharmacokinetic Covariate Model. Journal of Pharmacokinetics and Pharmacodynamics. 2004;31(2):109-34.