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RE: How to think about the different determination methods?

From: Ribbing, Jakob <Jakob.Ribbing>
Date: Tue, 9 Feb 2010 15:23:37 -0000

Dear Ye hong bo,


If I understand you correctly no single sample has been assayed with =
multiple assay methods? It may be that the assay method only makes a =
small contribution to the overall residual, but if you have enough =
information on the three SIGMAs you may keep it as three separate error =
magnitudes (however, the relative precision of assay methods will be =
confounded by that one centre may handle their sample collection etc. =
more accurate than another)


As I see it there are two ways to go:


Either start out with a simpler model by fixing OMEGAS to zero where you =
do not have enough information to describe IIV. It is rare that there is =
enough information to estimate separate etas for inter-compartmental =
clearance parameters (Q:s), so you may consider using the same eta or =
fixing one OMEGA to zero there.

Also, unless you have good information on the three individual volume =
parameters you may start out by only having an eta on the total volume =
(VSS below) and estimate the total volume and the fractions of that =
volume that represents the central and one of the peripheral volumes =
(FVC and FVP1 below). You can then proceed by allowing etas on one or =
both of these fractions according to the code below (estimating OMEGA4 =
and OMEGA6). An OMEGA BLOCK to estimate the covariance across (etas on) =
CL and volume parameters may further stabilize the model, if that =
correlation is important.




  DENOM = 1 + EXP(PHI + ETA(4))



  PHI2 = LOG(TFVP1/(1-TFVP1))

  DENOM2= 1 + EXP(PHI2 + ETA(6))

  FVP1 = EXP(PHI2 + ETA(6)) / DENOM2


  FVP = 1 - FVC



  FVP2 = 1 - FVP1

 V3 = FVP1 * VP

 V4 = FVP2 * VP


For the above code, FVC and FVP1 are estimated with a =
logit-transformation which is necessary only when adding etas on these =
parameters. Also, the logit code used above is a little more complex =
than needed, with the benefit that THETA(4) and THETA(6) above represent =
the typical fraction, rather than some value on the logit scale. For =
alternative 2 below this parameterisation is not suitable as it does not =
allow MU modelling (I think). The standard way of implementing the logit =
transformation gives exactly the same fit and allows for MU modelling.


Else (alternative 2), estimate your model using the new Monte Carlo =
methods in NONMEM 7. You can investigate large OMEGA BLOCKs to find out =
where you have important eta correlations, but for parameters where you =
have little or no information on the individual level you may have to =
fix OMEGA to a small value (e.g. 10 or 15% CV, which is biologically =
more plausible than no variability at all, and still efficient using =
Monte Carlo methods). However, it is not straight forward to use these =
estimation methods in nonmem, so allow ample time for getting yourself =
acquainted with these (settings for the various estimation methods that =
are appropriate for your data and model + implementing MU modelling in =
your control stream).


I hope this helps and wish you a happy New Year!




From: owner-nmusers
On Behalf Of yhb5442387
Sent: 09 February 2010 14:03
To: nmusers
Subject: [NMusers] How to think about the different determination =


Dear NMusers:

   I am dealing with the ppf(Propofol) data collected from 3 different =
centers,in which the drug concentrations ananlysis happens to be 3 =
different assays.Those are GC,Hplc-UV,HPlc-fluorescence,separaterly.As a =
item,the assay way is included,labeled as 1,2,3,in order.

And as an introduction from the Mannual, the assay way is arranged as =
the intraindividual variability .The syntax is as follows:

IF (ASSY.EQ.1) Y=F*(1+EPS(1))

IF (ASSY.EQ.2) Y=F*(1+EPS(2))

IF (ASSY.EQ.3) Y=F*(1+EPS(3))

And by the way,the pharmacokinetics of ppf were described by a =
three-compartment model.So the subroutine of advan 11,trans 4 was =

Of course,the combined Additive and CCV error model were considered at =
the beginning,but it seems to me that the additive error was so little =
(0.00001) that even could be ignored.So the CCV model was applied =
finally,as mentioned above.

So there are 6 thetas(Cl,V1,Q2,V2,Q3,V3),6 etas (exp ISV model) and 3 =
eps in the base model.Then the problem happened.

No matter what intial estimates I tried,the results of $EST and $COV =
steps allways indicate that the model was overparactermized.

The hint of R Matrix is either singular or NON-positive semidefinite =
appeared in the output files.And from the PDx-plotter,the plot of =
objective function Vs iteration was fairly flat.So I am confirmed that =
the model was overparactermized.In addtition,I have checked the R matrix =
in which some values in the line of SG22,SG33,are 0.

Here are my questions:

Should I take the assay error as an intraindividual variability?

How about If I take it as a covariate which would have an influence on =
any parameter of CL,V,and such and so on?

If there is only one eps in the intraindividual model, without the =
consideration of asssy error.Does it sounds reasonable?


Thank you for any comments:

This is my last email at this year.Because next several days are the =
Chines traditional Spring Festival.And I will be far away from the

laboratory and stay with my families for celebration.So,taking such a =
special opportunity,I would say thanks to whom help me before ,now

and soon.

Also, BEST WISHES TOO ALL THE NMusers.Happy Spring Festival!!!

Yours sincerely,Ye hong bo.






Received on Tue Feb 09 2010 - 10:23:37 EST

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