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RE: Large errors in the estimation of volume of distribution (Vd) for sparse data

From: Abu Helwa, Ahmad Yousef Mohammad - abuay010 <ahmad.abuhelwa >
Date: Mon, 9 Nov 2015 21:33:34 +0000

Hi Mathew,

Have you tried using an exponential model for vd ? like this: Vd = TEHTA=


From: owner-nmusers
 Behalf Of HUI, Ka Ho
Sent: Tuesday, 10 November 2015 1:13 AM
To: nmusers
Subject: [NMusers] Large errors in the estimation of volume of distribution=
 (Vd) for sparse data

Dear all,

I have some population PK data which are in general very sparse (95% have o=
nly 1 blood sample between 2 successive doses). I developed a population PK=
 model with the one-compartment model with 1st order absorption. The progre=
ss is generally okay except that whenever a random effect, i.e. *(1+ETA(1))=
, is used to describe distribution of Vd, OMEGA would be estimated to be ve=
ry large (around 45% in terms of CV, with 80% Shrinkage), despite statistic=
al significance (dOF approx. -5.5). So I dropped the random effect and expr=
essed Vd in terms of a single fixed effect. When the final model has come o=
ut, I performed bootstrap and found that most estimates are accurate except=
 Vd, which has a very large standard error and bias (mean 232, bias 49, SE =
156), while the estimates for CL and other parameters look normal. I then c=
onstructed the predictive plots for the developed model using both the orig=
inal estimates (i.e. estimates using my original dataset) (#1) and estimate=
s from one of the bootstrap runs which has an extreme estimate of Vd (9xx) =
(#2), and found out that the two plots of plasma profiles are quite differe=
nt in terms of the shape (#1 is "taller", #2 is much flatter) but have simi=
lar average Cp.

These seem to be suggesting that given my sparse data, it is impossible to =
require accurate estimations of both CL and Vd. Apart from fixing Vd to a f=
ixed value, is there any other possible solutions? Or is there anything tha=
t I might have overlooked?

Thanks and regards,

Received on Mon Nov 09 2015 - 16:33:34 EST

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