Dear Leonid yes, this is another way to move forward, similar as fixing eta to estimate from rich data.
________________________________ From: Leonid Gibiansky <lgibian...@quantpharm.com> To: Ethan Wu <ethan.w...@yahoo.com> Cc: Jurgen Bulitta <jbuli...@ordwayresearch.org>; "nmusers@globomaxnm.com" <nmusers@globomaxnm.com>; Roger Jelliffe <jelli...@usc.edu>; "Neely, Michael" <mne...@usc.edu> Sent: Wednesday, June 17, 2009 4:30:08 PM Subject: Re: [NMusers] estimating Ka from dataset combining rich sample study and sparse sampling study You can try IF(RICH data) THEN KA=THETA(1)*EXP(ETA(1)) ELSE KA=THETA(1) ENDIF -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 Ethan Wu wrote: > Dear Juergen, > thanks for your comment. > I was actually not aware such full non-parametric approach, apology for my >ignorance. the approach is very intersting, I will try to understand it more. > with regards to non-parametric approach, I was thinking alone the line of >estimation method for Eta only as offered in nonmem. > so I went ahead tried $NONPARAMETRIC UNCONDITIONAL option, but the Eta for >Ka still estimated to be very small, 5.50E-08 vs 0.13 estimated by using rich >data only. > > ------------------------------------------------------------------------ > *From:* Jurgen Bulitta <jbuli...@ordwayresearch.org> > *To:* Ethan Wu <ethan.w...@yahoo.com> > *Cc:* "nmusers@globomaxnm.com" <nmusers@globomaxnm.com>; Roger Jelliffe > <jelli...@usc.edu>; "Neely, Michael" <mne...@usc.edu> > *Sent:* Wednesday, June 17, 2009 2:42:31 PM > *Subject:* RE: [NMusers] estimating Ka from dataset combining rich sample > study and sparse sampling study > > Dear Ethan, > > > Your first suggestion would be a pragmatic way of moving forward. > > I have no personal experience with the hybrid method. > > Your third suggestion, using a full non-parametric approach > > should work better and is mathematically more consistent. > > This approach should not suffer from shrinkage. > > > I would expect this algorithm to behave as follows: > > 1) The subjects with rich data should be essentially completely > > unaffected by the subjects with sparse data. > > 2) The subjects with sparse data should have posterior (i.e. intra-individual) > > probability distributions of Ka which are similar to the inter-individual > > distribution of Ka for the population of subjects with rich data. > > > Depending on how the distribution of individual Ka values of > > the subjects with rich data look, you may or may not get a > > multimodal intra-individual distribution of Ka for the patients > > with sparse data. This may become important for the covariate > > relationships which you are trying to develop subsequently. > > > Please let me know, if Roger’s group or I can be of help to set > > you up, if you want to use NPAG for solving this task. > > > Best wishes > > Juergen > > > > *From:* owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] > *On Behalf Of *Ethan Wu > *Sent:* Wednesday, June 17, 2009 11:21 AM > *To:* nmusers@globomaxnm.com > *Subject:* [NMusers] estimating Ka from dataset combining rich sample study > and sparse sampling study > > > Dear all, > > I am working on this pop PK analysis. the objective is, to explore some >covariates on the exposure. > > the dataset has rich sampled study, with absorption phase well captured. and >also sparse sampling study with only trough sample, and another sample around >1-2hr after dosing > > with rich sample study data, the ka and eta on Ka is well estimated using >FOCE INT method and 1ct 1st order model. > > but when with pooled dataset, using the same model and method, eta on Ka is >estimated to be almost 0, the fit to the data from rich sampled study became >little worse on the peak. > > Is there way to keep a good estimation of Eta on Ka, which is to make sure >the good capture of Cmax, at least for rich sampled subjects? > > > with my limited knowledge, I was thinking: > > -- fixing Eta on ka with the estimate from rich sample study alone > > -- hybrid estimating methods > > -- nonparametric method > > > Any comments will be highly appreciated. > > > > >
