From: Pyry Välitalo <*pyry.valitalo*>

Date: Thu, 16 Feb 2017 13:07:01 +0200

Dear NMusers,

Based on the NONMEM Inter-Occasion Variability (IOV) example control

stream, located at NONMEM_install_dir/examples/example7.ctl, it seems that

it is currently not possible to Mu parameterize IOV in NONMEM 7.3. The

relevant lines from example7.ctl control stream are copypasted below:

MU_2=THETA(2)

CLB=DEXP(MU_2+ETA(2))

DCL1=DEXP(ETA(3))

DCL2=DEXP(ETA(4))

DCL3=DEXP(ETA(5))

DCL=DCL1

IF(TIME.GE.5.0) DCL=DCL2

IF(TIME.GE.10.0) DCL=DCL3

CL=CLB*DCL

My question is: Does this mean that the updating procedure of IIV/IOV

parameters (omegas) does not have the EM efficiency when IOV is present? If

we had no IOV, then I think NONMEM would calculate for the update step that

THETA(2)=E(LOG(CLB))

OMEGA(2,2)=VAR(LOG(CLB))

where E() refers to computing mean and VAR() refers to computing variance.

These expressions are useful, because with these there is no need to find

the best THETA(2) or OMEGA(2,2) values with numerical optimization. It

saves computation time that just mean and variance can be taken.

Now, with the IOV model, I think the updating could be accomplished with

THETA(2)=E(LOG(CLB))

OMEGA(2,2)=VAR(LOG(CLB))

OMEGA(3,3)=VAR(LOG(CL))-OMEGA(2,2)

However, I'm not sure what actually happens inside NONMEM.

-Is the updating done like this, or does the lack of MU referencing in IOV

force the estimation process to use gradient descent for estimation of

some/all parameters?

-If gradient descent is used, how dramatically does it reduce the speed of

the estimation process?

-If MU referencing is impossible with the presence of IOV, would it then be

possible to use occasion variable OCC as the ID, and somehow convince

NONMEM not to reset the compartments when OCC changes? This way, MU

referencing could be used for the OCC variable, and IIV would be just

ignored for that run. This would be only for exploratory runs, of course.

For this discussion, let's assume that

-Adequate data exist to estimate occasion-specific random effects.

-The IOV parameters are critical for the model to be able to describe the

data nicely (e.g. large IOV in absorption)

Looking forward to hear your thoughts! Best wishes,

Pyry Välitalo

Postdoc, Leiden University, Division of Pharmacology

Senior Scientist, Orion Pharma, Drug Disposition and Pharmacometrics

Received on Thu Feb 16 2017 - 06:07:01 EST

Date: Thu, 16 Feb 2017 13:07:01 +0200

Dear NMusers,

Based on the NONMEM Inter-Occasion Variability (IOV) example control

stream, located at NONMEM_install_dir/examples/example7.ctl, it seems that

it is currently not possible to Mu parameterize IOV in NONMEM 7.3. The

relevant lines from example7.ctl control stream are copypasted below:

MU_2=THETA(2)

CLB=DEXP(MU_2+ETA(2))

DCL1=DEXP(ETA(3))

DCL2=DEXP(ETA(4))

DCL3=DEXP(ETA(5))

DCL=DCL1

IF(TIME.GE.5.0) DCL=DCL2

IF(TIME.GE.10.0) DCL=DCL3

CL=CLB*DCL

My question is: Does this mean that the updating procedure of IIV/IOV

parameters (omegas) does not have the EM efficiency when IOV is present? If

we had no IOV, then I think NONMEM would calculate for the update step that

THETA(2)=E(LOG(CLB))

OMEGA(2,2)=VAR(LOG(CLB))

where E() refers to computing mean and VAR() refers to computing variance.

These expressions are useful, because with these there is no need to find

the best THETA(2) or OMEGA(2,2) values with numerical optimization. It

saves computation time that just mean and variance can be taken.

Now, with the IOV model, I think the updating could be accomplished with

THETA(2)=E(LOG(CLB))

OMEGA(2,2)=VAR(LOG(CLB))

OMEGA(3,3)=VAR(LOG(CL))-OMEGA(2,2)

However, I'm not sure what actually happens inside NONMEM.

-Is the updating done like this, or does the lack of MU referencing in IOV

force the estimation process to use gradient descent for estimation of

some/all parameters?

-If gradient descent is used, how dramatically does it reduce the speed of

the estimation process?

-If MU referencing is impossible with the presence of IOV, would it then be

possible to use occasion variable OCC as the ID, and somehow convince

NONMEM not to reset the compartments when OCC changes? This way, MU

referencing could be used for the OCC variable, and IIV would be just

ignored for that run. This would be only for exploratory runs, of course.

For this discussion, let's assume that

-Adequate data exist to estimate occasion-specific random effects.

-The IOV parameters are critical for the model to be able to describe the

data nicely (e.g. large IOV in absorption)

Looking forward to hear your thoughts! Best wishes,

Pyry Välitalo

Postdoc, Leiden University, Division of Pharmacology

Senior Scientist, Orion Pharma, Drug Disposition and Pharmacometrics

Received on Thu Feb 16 2017 - 06:07:01 EST