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 =

your control stream).

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?

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.

--

工作和生活,都要开心的过.

你好,叶红波在此送上真挚的=

祝福.祝你开心,

叶红波

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

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 =

your control stream).

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?

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.

--

工作和生活,都要开心的过.

你好,叶红波在此送上真挚的=

祝福.祝你开心,

叶红波

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