RE: Choice of models

________________________________________ From: [email protected] [[email protected]] on behalf of Toufigh Gordi [[email protected]] Sent: Tuesday, January 24, 2012 5:23 AM To: [email protected] Subject: [NMusers] Choice of models Dear all, I have a general question on the choice of model in a population analysis. I have a set of data set that includes a large number of studies with about ¾ of the data from extensive sampling schemes (phase 1, 2, and 3 studies) and the rest from sparse samples (phase 3 clinical studies). When developing the PK model, a model on the extensive samples only fits the data well and I can get quite reasonable parameter estimates, including covariate effects, and a successful $COV (NONMEM). When all data is used, the model becomes somewhat instable: the same covariates are identified but the model becomes quite sensitive to the initial estimates and the $COV step won’t go through. I could, of course, perform a bootstrap to go around this issue. In general, the fit of the model based on the full data set is not as good as the extensive data set model, although the two models are rather similar with regard to the parameter estimates. However, the range of estimated parameters is wider when using all data and noticeably KA and V2 are skewed to very larger values. Moving forward, I could either use the full data model and simulate steady state profiles for the phase 3 study (sparse samples) data. Or, I could use the model based on the extensive samples only, use the sparse data and generate post-hoc estimates for the sparsely sampled individuals and move forward that way. The advantage with the first option is that all the available data have been used in the modeling process. The disadvantage would be that the model is not as good as the other model, with sparse data distorting the parameter estimates. The advantage of the second option is that the model performs better and there is really no reason why the underlying PK model for the sparsely sampled subjects should be different, which means one should be able to use that model to generate post-hoc estimates. The disadvantage is that not all the available data have been used in the model building process. It would be interesting to hear other people’s thoughts and ideas on this. Toufigh ________________________________