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RE: Questions about identifiability

From: Xiao, Alan <Alan.Xiao>
Date: Fri, 13 Apr 2007 08:47:37 -0400

Amy,

Just one comment about identifiability. A very simple and efficient way =
to see whether your model has identifiability problem is to randomly =
change your initial guesses to see whether your parameter estimates are =
stable. In addition, that your simulation proves no identifiability =
problem does not necessarily mean your model will not have =
identifiability problem to your real data.

Alan

-----Original Message-----
From: owner-nmusers
[mailto:owner-nmusers
Sent: Friday, April 13, 2007 4:45 AM
To: Silke.Dittberner
Cc: nmusers
Subject: Re: [NMusers] Questions about identifiability


Dear Silke,

Before looking into the identifiability question, it is useful to know
what the differential equations and the dosing route are, please?

Kind regards,

Amy

-------------------------------------------------------------------------=
------
S.Y.A. Cheung
Postgraduate Research Student
The Centre for Applied Pharmacokinetic Research (CAPKR)
School of Pharmacy and Pharmaceutical Sciences
University of Manchester
Stopford Building
Oxford Road
Manchester
U.K.

-------------------------------------------------------------------------=
----------------------------------------------

On 13/04/07, Silke.Dittberner
<Silke.Dittberner
>
>
> Dear NONMEM users,
>
> The PK of the compound we are working on can be described by a =
2-compartment
> model with non–linear protein binding in the central and in the =
peripheral
> compartment, which from a physiological point of view makes complete =
sense.
> The question we have is whether such model is identifiable having just =
total
> plasma concentration (no binding information is available).
>
> Therefore we want to simulate different kind of datasets and check if =
NONMEM
> is able to re-estimate them properly.
>
>
> · Our first question was: "Is the structure itself in =
principle
> identifiable?"
>
> We simulated a dataset with 100 time points per subject and no
> intra- or inter-individual variability and no residual error. ('ideal' =
data:
> plenty time points, no random error) Since under these conditions =
the
> parameters could be re-estimated (parameter estimates were nearly =
identical
> to the original ones, %SE is very small) we concluded that the =
structure
> in principle is identifiable.
>
>
>
> · Our second question was: "Are the time points of the given =
study
> sufficient to estimate all parameters assuming 'ideal' data?"
>
> We simulated the given dataset assuming no intra- or
> inter-individual variability and no residual error. The parameter =
estimates
> were again nearly identical to the original ones and %SE is still =
very
> small (below 0.3 %).
>
> · Our third question was: "Could the parameters still be =
re-estimated
> if we assume inter- and intra-subject variability for the simulation =
step?"
>
> We simulated the given dataset assuming IIV, IOV and residual =
error.
> Under these conditions, the parameter (fixed and random effect) =
estimates
> are again similar, but not identical to the original ones, %SE =
increased to
> about 9% (one exception is the SE% of the parameter for the =
amount of
> peripheral binding sites which were estimated to be 16%). However, =
when we
> re-estimate omitting the IIV and IOV, the estimated parameters differ =
from
> the original ones and estimates for the peripheral binding becomes =
difficult
> to estimate.
>
> The questions we have are:
> 1. Are these experiments sufficient to conclude on the model
> identifiability?
> 2. Does it make sense that the fixed effect parameters differ =
from the
> original ones when IIV and IOV are omitted in the estimation step in
> constrast to when they are included in the simulation step? Shouldn't =
the
> structure of the model remain stable?
>
> 3. How often would you simulate and re-estimate the third =
experiment?
> 4. Would you vary the initial estimates to check for any =
potential
> other set of parameters? (If yes how often?)
> 5. One problem is that the complete model with IIV and IOV has =
quite
> long run times (around 24h), do you think checking the model with just =
IIV
> would be enough?
>
> 6. Do you have any other proposal to check for the =
identifiability of a
> model?
>
> Your help is highly appreciated, thank you in advance,
>
> Silke
>
>
>
> Silke Dittberner
> PhD student
> Institute of Pharmacy
> University Bonn
> Germany


--
Received on Fri Apr 13 2007 - 08:47:37 EDT

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