Re: 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 <jbulitta
> *To:* Ethan Wu <ethan.wu75
> *Cc:* "nmusers
> <jelliffe
> *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-nmusers
> [mailto:owner-nmusers
> *Sent:* Wednesday, June 17, 2009 11:21 AM
> *To:* nmusers
> *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.
>
>
>
>
>
>
>
>