Re: Describing variability

From: Nick Holford Subject: Re: [NMusers] Describing variability Date:Tue, 01 Apr 2003 07:12:23 +1200 Justin, Your question about using SAME with BOV is a good one. It is making the assumption that BOV is constant across all occasions but you need to understand exactly what is the same. It is NOT the value of ETA but the value of OMEGA ie. the variance of the distribution from which ETA is sampled randomly on each occasion. So on each occasion a new ETA is used but it comes from the same distribution as other occasion ETAs. If you choose to not use the SAME option but instead specify a different OMEGA for each occasion then the you will still get a different ETA for each occasion but perhaps you would get more variability in the ETAs on the 2nd occasion compared with the first because OMEGA is bigger for OCC=1 compared to OCC=2. I find it hard to think of a situation where you would assume that the size of the random variability varied from occasion to occasion. Remember you are assuming that the average variability on each occasion is zero. If you think there is a systematic change so that the average value of the parameter changes with occasion then you should code this as a function of THETA and OCC. I have done some limited testing of estimating BOV with and without SAME. I could find no real difference in the results when the data was simulated with SAME. The main difference is that you have extra OMEGA parameters to estimate and run times will be longer. So the bottom line is use the SAME option unless you can think of a good reason not to. The definition of occasion is a personal choice. I like to think that CL may vary from dose to dose so I choose each new dose interval with one or more conc measurements as an occasion. Why use FOCE? Because it is a better method. FO is quick and dirty. You may be lucky and the results may the same as FOCE but if they differ then the FOCE results are more likely to be a better reflection of reality. In my experience FO produces very much larger estimates of OMEGA than FOCE. I do not trust FO. I do not worry too much about convergence as long as the graphical fits look good and the parameter estimates are reasonable in a mechanistic sense. Remember that all the published data comparing FO and FOCE has had to rely on simulations with well behaved distributions and in all cases I know of simple models. Real data is often quite different. I put my faith in the theoretical expectation that FOCE is intrinsically a better algorithm rather than rely on some simple simulations that show FO and FOCE dont seem to be very different. Nick -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/