# 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.

TVFVC = THETA(4)

PHI = LOG(TVFVC/(1-TVFVC))

DENOM = 1 + EXP(PHI + ETA(4))

FVC = EXP(PHI + ETA(4)) / DENOM

TFVP1 = THETA(6)

PHI2 = LOG(TFVP1/(1-TFVP1))

DENOM2= 1 + EXP(PHI2 + ETA(6))

FVP1 = EXP(PHI2 + ETA(6)) / DENOM2

FVP = 1 - FVC

V2 = FVC*VSS

VP = FVP*VSS

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 =

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

Jakob

________________________________

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

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 =
applied.

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?

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.

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

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