From: Matt Hutmacher <*matt.hutmacher*>

Date: Tue, 26 Nov 2013 09:41:17 -0500

Hi Jacob, and everyone,

Sorry to be unclear and if I have added any confusion. My derivation =

was for the oral/SC administration (into a depot compartment) case with =

no IV data and with no extra CL/V correlation. If there were extra =

correlation, the OMEGA matrix would look like

V11+VFF VFF+COV(eta1,eta2) VFF+COV(eta1,eta3) =

VFF+COV(eta1,eta4)

VFF+COV(eta2,eta1) V22+VFF VFF+COV(eta2,eta3) =

VFF+COV(eta2,eta4)

VFF+COV(eta3,eta1) VFF+COV(eta3,eta2) V33+VFF VFF =

VFF+COV(eta3,eta4)

VFF+COV(eta4,eta1) VFF+COV(eta4,eta2) VFF+COV(eta4,eta3) V44+VFF

which would not be identifiable without the IV data. In my opinion, if =

there is no IV data, the F is really just conceptual. It is a a way of =

thinking about certain covariates that affect both CL and V etc in an =

identical way. Parameterization using covariates (which I do often) and =

an eta on F is just a trick (in the no-IV data case) to get the OMEGA =

matrix as previously defined and to avoid having to specify eg, =

CL=THETA(1)/(1+THETA(2)*FOOD) V=THETA(3)/(1+THETA(2)*FOOD), in the =

model (which is equivalent). In this case, I am concerned about adding =

the extra eta on F to constrain the OMEGA matrix because of the whole =

identifiability issue. Plots would certainly be affected (there really =

aren't 3 etas in the non-IV data case). In there is no =

extra-correlation, and F is inducing a high degree of correlation, one =

might consider putting the eta's on V, K, K12 and K21. The variability =

of F would be lumped into V, and this would cancel from the K's allowing =

a diagonal matrix (note that one would need to be careful how one =

parameterized this and it does not preclude evaluating and estimating =

fixed effects on CL, V, etc.)

Best,

Matt

(I have trimmed some of the earliest emails from this note to ensure =

delivery).

-----Original Message-----

From: owner-nmusers

On Behalf Of Ribbing, Jakob

Sent: Tuesday, November 26, 2013 05:46

To: Mueller-Plock, Nele; Leonid Gibiansky; 'nmusers'

Cc: Ribbing, Jakob

Subject: RE: [NMusers] Getting rid of correlation issues between CL and =

volume parameters

Hi Nele,

I believe Matt's point was more to the situation where any remaining =

correlation between CL and V random components can not be accounted for =

by covariates, so that both eta on F and block2 on CL and V is used?

If eta on F and covariates takes care of the correlation between CL and =

V: I would say that you may get even more informative diagnostics with =

this implementation.

For example, if you have not yet taken dose/formulation into account and =

this affects only F, it would come out as a clearer trend on the eta1 =

(relative F). This would help in interpretation (but I would highlight =

Nick's earlier point that eta on F may capture other nonlinearities that =

are shared between CL and V; like degree of protein binding for a =

low-extraction drug).

Best

Jakob

-----Original Message-----

From: owner-nmusers

On Behalf Of Mueller-Plock, Nele

Sent: 26 November 2013 08:21

To: Leonid Gibiansky; 'nmusers'

Subject: RE: [NMusers] Getting rid of correlation issues between CL and =

volume parameters

Dear all,

Thanks for picking up this discussion, and bringing in so many points of =

view.

When I started the discussion I had in mind the physiological viewpoint, =

from which we know that if there is between-subject variability in F1, =

this must result in a correlation between volume and CL parameters. From =

the discussions I would conclude that the group would favor to account =

for this correlation via inclusion of ETA on F1 and then a coding of

FF1=EXP(ETA(1))

CL=THETA()*EXP(ETA())/FF1

V=THETA()*EXP(ETA())/FF1

whereas this does not mean that there is no additional correlation =

between the parameters which needs to be accounted for in the =

off-diagonal OMEGA BLOCK structure? Also, I am afraid I was not able to =

completely follow Matt's argumentation, but would also be interested to =

hear if implementing the code above might lead to misleading plots.

Thanks and best

Nele

Received on Tue Nov 26 2013 - 09:41:17 EST

Date: Tue, 26 Nov 2013 09:41:17 -0500

Hi Jacob, and everyone,

Sorry to be unclear and if I have added any confusion. My derivation =

was for the oral/SC administration (into a depot compartment) case with =

no IV data and with no extra CL/V correlation. If there were extra =

correlation, the OMEGA matrix would look like

V11+VFF VFF+COV(eta1,eta2) VFF+COV(eta1,eta3) =

VFF+COV(eta1,eta4)

VFF+COV(eta2,eta1) V22+VFF VFF+COV(eta2,eta3) =

VFF+COV(eta2,eta4)

VFF+COV(eta3,eta1) VFF+COV(eta3,eta2) V33+VFF VFF =

VFF+COV(eta3,eta4)

VFF+COV(eta4,eta1) VFF+COV(eta4,eta2) VFF+COV(eta4,eta3) V44+VFF

which would not be identifiable without the IV data. In my opinion, if =

there is no IV data, the F is really just conceptual. It is a a way of =

thinking about certain covariates that affect both CL and V etc in an =

identical way. Parameterization using covariates (which I do often) and =

an eta on F is just a trick (in the no-IV data case) to get the OMEGA =

matrix as previously defined and to avoid having to specify eg, =

CL=THETA(1)/(1+THETA(2)*FOOD) V=THETA(3)/(1+THETA(2)*FOOD), in the =

model (which is equivalent). In this case, I am concerned about adding =

the extra eta on F to constrain the OMEGA matrix because of the whole =

identifiability issue. Plots would certainly be affected (there really =

aren't 3 etas in the non-IV data case). In there is no =

extra-correlation, and F is inducing a high degree of correlation, one =

might consider putting the eta's on V, K, K12 and K21. The variability =

of F would be lumped into V, and this would cancel from the K's allowing =

a diagonal matrix (note that one would need to be careful how one =

parameterized this and it does not preclude evaluating and estimating =

fixed effects on CL, V, etc.)

Best,

Matt

(I have trimmed some of the earliest emails from this note to ensure =

delivery).

-----Original Message-----

From: owner-nmusers

On Behalf Of Ribbing, Jakob

Sent: Tuesday, November 26, 2013 05:46

To: Mueller-Plock, Nele; Leonid Gibiansky; 'nmusers'

Cc: Ribbing, Jakob

Subject: RE: [NMusers] Getting rid of correlation issues between CL and =

volume parameters

Hi Nele,

I believe Matt's point was more to the situation where any remaining =

correlation between CL and V random components can not be accounted for =

by covariates, so that both eta on F and block2 on CL and V is used?

If eta on F and covariates takes care of the correlation between CL and =

V: I would say that you may get even more informative diagnostics with =

this implementation.

For example, if you have not yet taken dose/formulation into account and =

this affects only F, it would come out as a clearer trend on the eta1 =

(relative F). This would help in interpretation (but I would highlight =

Nick's earlier point that eta on F may capture other nonlinearities that =

are shared between CL and V; like degree of protein binding for a =

low-extraction drug).

Best

Jakob

-----Original Message-----

From: owner-nmusers

On Behalf Of Mueller-Plock, Nele

Sent: 26 November 2013 08:21

To: Leonid Gibiansky; 'nmusers'

Subject: RE: [NMusers] Getting rid of correlation issues between CL and =

volume parameters

Dear all,

Thanks for picking up this discussion, and bringing in so many points of =

view.

When I started the discussion I had in mind the physiological viewpoint, =

from which we know that if there is between-subject variability in F1, =

this must result in a correlation between volume and CL parameters. From =

the discussions I would conclude that the group would favor to account =

for this correlation via inclusion of ETA on F1 and then a coding of

FF1=EXP(ETA(1))

CL=THETA()*EXP(ETA())/FF1

V=THETA()*EXP(ETA())/FF1

whereas this does not mean that there is no additional correlation =

between the parameters which needs to be accounted for in the =

off-diagonal OMEGA BLOCK structure? Also, I am afraid I was not able to =

completely follow Matt's argumentation, but would also be interested to =

hear if implementing the code above might lead to misleading plots.

Thanks and best

Nele

Received on Tue Nov 26 2013 - 09:41:17 EST