Dears, Sometimes assigning different residual variability for rich vs sparse data helps to make the model more stable.
Regards, Juan. From: [email protected] [mailto:[email protected]] On Behalf Of Lavigne, Jean Sent: martes, 24 de enero de 2012 8:21 To: Toufigh Gordi; [email protected] Subject: RE: [NMusers] Choice of models Dear Toufigh, Here are some of my thoughts: 1 - You may have an issue of quality of your data with the sparse samples (patients with un-accurate recording of evens like dosing and sampling) 2 - You have not specified which method of estimate that was used with NONMEM (FO, FOCE, Bayesian, etc...), I would try with a Bayesian approach from your best model with rich samples. 3 - If you use post-hoc estimates for your sparse data, you may want to double check if they are different from the rest of the population Best regards, Jean From: [email protected] [mailto:[email protected]] On Behalf Of Jan-Stefan Van der Walt Sent: Tuesday, January 24, 2012 5:24 AM To: Toufigh Gordi; [email protected] Subject: Re: [NMusers] Choice of models Hi Toufigh, Recently I used the 90% prediction interval (generated by an appropriately binned VPC) of the rich data (three studies with observed doses) to evaluate the sparse data (one sample on 4 occasions). The sparse data contained more information about the covariates of interest, but the dosing was unobserved. I analysed the rich and sparse data simultaneously first including and then excluding the sparse data outside the 90% PI and compared the results. The eta-shrinkage values decreased considerably when the observations outside the 90% PI were excluded and I had more confidence in the covariate relationships. As a side issue, I estimated a time-after-dose for the observations outside the 90% PI. It was interesting that the difference between the reported and estimated dosing times seemed to increase as the trial progressed (0.92h [month 6], 1.05h [month 12]), 1.11h [month 18] and 3.6h [month 24]. Hope this helps. Regards, Jan-Stefan On 24 January 2012 05:05, Denney, William S. <[email protected]<mailto:[email protected]>> wrote: Hi Toufigh, I typically think that data quality decreases with phase and with sampling frequency. Given what you described below, I'd think that you're fighting data quality in the sparse, phase 3 studies, and with the parameters you're describing as having trouble, it seems to support that thought. Were I to guess, you could probably pick out the most influential 3% of sparse samples (arbitrary percentage), and look at them in more detail and find that they look more like Cmax than Ctrough or something such the time since last dose appears to be off. Beyond that, philosophically, I think that trough concentrations should not be allowed to affect Ka because the effect is usually so small as not to be measurable (assuming that we're discussing a drug with a reasonable separation between the alpha elimination phase and measurement time). Thanks, Bill On Jan 23, 2012, at 11:45 PM, "Toufigh Gordi" <[email protected]<mailto:[email protected]>> wrote: 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. 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