RE: PK models for rich and sparse sampling

From: mark.e.sale@gsk.com Subject: RE: [NMusers] PK models for rich and sparse sampling Date: Sun, May 30, 2004 2:12 pm At the risk of making a CLM (Career Limiting Move), I'm going to disagree with the FDA (Food and drug administration). You can see that we at GSK (GlaxoSmithKline) are fond of our TLAs (Three Letter Acronyms). I'll try to avoid TLA speak. Combining the data sets is certainly an option, one we use frequently. But it isn't clear that it is (always) the best. Limitations include computational time. Perhaps more important is the possibility that the rich data set will dominate the estimation methods, resulting in very little being learned from the sparse data set. This would happen particularly if the rich data set is larger. I'd propose considering another methods we've had some success with in constructing very complex models. This is a much more Bayesian approach. Essentially, you use the PRIOR functionality in NONMEM, setting the initial estimates for THETA and OMEGA to a distrib! utions derived from the rich data set. So, if you find that K23 has a mean of 0.5 and a SEE of 0.2 you specify that using PRIOR. In this way, if the second (sparse) data set is uninformative about K23, then the estimates of K23 (including the SEE) are unchanged, but other parameters (for which the sparse data set is informative) will be essentially reestimated, using what you've learned from the first data set. It also adds very little computational time to the analysis. Let me know if you need help for the use of PRIOR hich is not documented in V5, but can be used.) Mark