RE: Best approach with limited data
I would use a population approach but would not pool the sparse data. I
think it would be appropriate to start with a 2 compartment model and
get the objective function. You can then try more compartment and see if
it improves the obj statistically. I have doubts about being able to fit
the 4 compartment model under that sampling design.
I would also perform a bootstrapping approach to assess quantitatively
the precision of all the model parameters.
Serge Guzy
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Quoted reply history
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Mulla Hussain - Senior Pharmacist
Sent: Wednesday, February 28, 2007 8:57 AM
To: [email protected]
Subject: [NMusers] Best approach with limited data
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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