Regarding the error message from PsN vpc: I can see from the message
that you are using a *very* old version of PsN. I suggest that you
install the latest version and try again.
Best regards,
Kajsa Harling
Quoted reply history
On 11/10/2015 05:12 AM, HUI, Ka Ho
wrote:
Thanks for your responses!
Nitin, I encountered an
error when generating VPC by PsN. It says No DV values
found after filtering original data.
At lib/tool/npc.subs.pm line 2215. What does
it mean?
Felix, Past published data suggested similar
parameter estimates and models compared to my final model.
This is PO and I fixed Ka at a pre-estimated value (So no
estimation of fixed or random effect).
Ahmad, Yes. The CV is even larger.
Matthew
From: Abu Helwa, Ahmad
Yousef Mohammad - abuay010
[mailto:ahmad.abuhelwa_at_mymail.unisa.edu.au]
Sent: Tuesday, November 10, 2015 5:34 AM
To: HUI, Ka Ho <matthew.hui_at_link.cuhk.edu.hk>;
nmusers_at_globomaxnm.com
Subject: RE: Large errors in the estimation of volume
of distribution (Vd) for sparse data
Hi Mathew,
Have you tried using an exponential model for
vd ? like this: Vd = TEHTA(1)*EXP(ETA(1))
Ahmad.
From: felix boakye-agyeman
[mailto:boakyefe_at_gmail.com]
Sent: Tuesday, November 10, 2015 12:41 AM
To: HUI, Ka Ho <matthew.hui_at_link.cuhk.edu.hk>
Subject: Re: [NMusers] Large errors in the estimation
of volume of distribution (Vd) for sparse data
Hello,
Do you have
historical data to compare you data to? (Do you know if you
are hitting a local minimum)
Is this iv or po, if
its po how is your Ka?
You may also be
over-parameterized due to your data
From: Kaila, Nitin
[mailto:Nitin.Kaila_at_pfizer.com]
Sent: Tuesday, November 10, 2015 12:14 AM
To: HUI, Ka Ho
<matthew.hui_at_link.cuhk.edu.hk>
Subject: RE: Large errors in the estimation of
volume of distribution (Vd) for sparse data
Matthew.
Construct
visual predictive check (VPC) plots, using all the estimates
of the bootstrap runs, as that will be a more true estimate
of overall variability in the Cp predictions.
Use the
rawres option in PsN to perform the VPC, and then compare
your original final model VPC plot with the VPC plot with
all estimates of the bootstrap.
Nitin
From:
owner-nmusers_at_globomaxnm.com
[mailto:owner-nmusers_at_globomaxnm.com]
On Behalf Of HUI, Ka Ho
Sent: Monday, November 9, 2015 9:43 AM
To: nmusers_at_globomaxnm.com
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 only 1
blood sample between 2 successive doses). I developed a
population PK model with the one-compartment model with 1st
order absorption. The progress 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
very large (around 45% in terms of CV, with 80% Shrinkage),
despite statistical significance (dOF approx. -5.5). So I
dropped the random effect and expressed Vd in terms of a
single fixed effect. When the final model has come out, 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 constructed
the predictive plots for the developed model using both the
original estimates (i.e. estimates using my original
dataset) (#1) and estimates 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
different in terms of the shape (#1 is taller, #2 is much
flatter) but have similar 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 fixed value, is there any other possible
solutions? Or is there anything that I might have
overlooked?
Thanks and regards,
Matthew