Re: Best approach with limited data
I am not sure that you can answer this question in general, without seeing the data. It may depend on the ratio of inter- to intra- individual errors. I would try it. One option could be to fix the variances of residual errors at values obtained from the full-profile subject. Also, you will need to restrict the number of etas (omegas) to at most 2 (one per measurement: not a firm rule but usually an upper estimate of the number of identifiable parameters). You may look on the fit or just make an educated guess where to put those etas. NONMEM (or bootstrap if you prefer to use CPU intensive methods) will give you precision of the estimates.
Leonid
Mulla Hussain - Senior Pharmacist wrote:
> Hi
>
> I have sparse data from n=69 individuals from 3 dose levels, and rich data from 1 individual at 1 dose level, following the deployment of a drug eluting device. In the 69 individuals I have a single plasma sample, and a single tissue sample (both collected at the same time point). The sample time points range from 0.25 to 120 days (post deployment), 2 individuals contributing at each time point. In the 1 individual I have greater than 8 sample points in each biophase.
>
> The rich data from the 1 individual suggests 2 compartment disposition in both plasma and tissue (a 4 compartment model described the data quite well in WinNonLin). Pooling the sparse data at each dose level, suggests 2 compartment disposition in both blood and plasma. My questions are:-
>
> 1) Would you take a population approach to this data? – can mean population parameters and their variability be robustly estimated?
>
> 2) Would you expect the parameters of a 4 cmpt model (8 thetas and at least 4 omegas) to be reliably estimated with this data?
>
> Any thoughts much appreciated.
>
> Thanks
>
> Hussain Mulla
>
> Department of Pharmacy
>
> University Hospitals of Leicester NHS Trust
>
> Glenfield Hospital
>
> Leicester
>
> England
>
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