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: Mon, May 31, 2004 8:05 am WRT (with regard to) Nicks comments: I don't disagree with combining data sets - we do this very frequently, and is our first choice. My concern was with the unqualified term "best". To elaborate, I think there are two situations when combining has problems: 1. Computational time is limited 2. We have seen that adding a new, sparse data set to a large, rich data sets results in very little change in parameter estimates, in spite of the fact that the post hoc etas for the new individuals are very different from zero. That is, the new data seem different, so the parameters from the first analysis (rich data) seem to not be correct, and yet the overall parameters seem to reflect only the first (rich) data set. The parameters are insensitive to the second data set, and so we are without an adequate description of the second data set. One option is to allow parameters that can be estimated from the second data set (here, presumably CL and V) to vary between data sets, while having common values for those not estimable from the second data set (here, presumably K23 and K32). In the example cited above, this didn't work, CL and V were very poorly estimated from the second data set alone. Another options is ! to! use the mean and SD of the post hoc estimates from the combined data sets to describe the second data set. Another option is the PRIOR method. Mark