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

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

2-compartment

peripheral

sense.

total

NONMEM

principle

data:

the

identical

structure

study

estimates

very

re-estimated

step?"

error.

estimates

increased to

amount of

when we

from

difficult

from the

the

experiment?

potential

quite

IIV

identifiability of a

--

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