Re: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[Pyry Välitalo]

Dear NMusers,

Documentation relevant to this discussion (Intro to NONMEM 7.5.1. p
185-186):
Time dependent covariates, or covariates changing with each record within
an individual, cannot
be part of the MU_ equation. For example
MU_3=THETA(1)*TIME+THETA(2)
should not be done. Or, consider
MU_3=THETA(2)*WT
Where WT is not constant within an individual, but varies with observation
record (time). This
would also not be suitable. However, we could phrase as
MU_3=THETA(2)
CL=WT*(MU_3+ETA(3))
where MU_3 represents a population mean clearance per unit weight, which is
constant with
time (observation record), and is more universal among subjects. The MU
variables may vary
with inter-occasion, but not with time.

Suppose we have a situation where WT has an unknown power term associated
with it modeled
as THETA(3) in this example:
CL=THETA(2)*WT**THETA(3)*EXP(ETA(1))
Normally, we could efficiently linear model this as follows:
MU_1=THETA(2)+THETA(3)*LOG(WT)
CL=EXP(MU_1+ETA(1))
with THETA(2) transformed into the log of clearance domain. However, if WT
changes record
by record within the individual, then LOG(WT) may not be in the Mu
modeling. We would then
remove the THETA(3)*LOG(WT) term from MU_1:
MU_1=LOG(THETA(2))
CL=WT**THETA(3)*EXP(MU_1+ETA(1))
And THETA(3) itself would not be MU modeled.

Best wishes Pyry

On Tue, 14 Jan 2025 at 23:00,  wrote:

> I got an email indicating my message below may not have gone through so,
> I’m sending it again.  Apologies if you receive this message twice.  -Ken
>
>
>
>
>
> Hi Bob,
>
>
>
> I agree with the sentiments of Nick’s email below that if EM and Bayes’
> methods are simply averaging the time-varying covariates and essentially
> treating them as time-invariant values set to the average covariate values,
> then this would be a MAJOR deficiency in how EM and Bayes methods in NONMEM
> handle time-varying covariates.  Afterall, the main reason we investigate
> time-varying covariates is to evaluate whether certain parameters can
> change over time within a subject where the time-varying covariates may
> help explain some of the within-subject variation (e.g., IOV).  If the EM
> and Bayes’ methods as implemented in NONMEM treat the time-varying
> covariates as time-invariant at the arithmetic mean value, then the
> predictions will not be properly considering these time-varying
> covariates.  However, I suspect that this is not the case, and the EM and
> Bayes’ methods as implemented in NONMEM are actually considering the
> time-varying nature of these covariates and the confusion comes from an
> ambiguous explanation of what NONMEM is doing.  Let me see if I can explain
> what NONMEM is doing, and you can correct me if I’m wrong or further
> elaborate on Nick’s and my concerns.
>
>
>
> I assume that EM and Bayes methods are actually using the time-varying
> covariates, however, the EM and Bayes’ methods perform centering and/or
> scaling based on the subject-specific arithmetic mean values of the
> covariates when mu-referencing is implemented.  This is to enhance
> numerical stability when estimating the fixed effects associated with
> time-varying covariates.  Thus, the EM and Bayes’ methods are actually
> using the time-varying values of the covariates in the prediction of the
> responses and the averaging of the covariates within a subject is only
> implemented for MU referencing.  Is my understanding correct?
>
>
>
> Thanks,
>
>
>
> Ken
>
>
>
>
>
>
>
> *From:* owner-nmus...@globomaxnm.com  *On
> Behalf Of *Nick Holford
> *Sent:* Monday, January 13, 2025 5:49 PM
> *To:* Bauer, Robert ; 'Leonid Gibiansky' <
> lgibian...@quantpharm.com>; Sébastien Bihorel <
> sebastien.biho...@regeneron.com>; nmusers@globomaxnm.com
> *Subject:* RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying
> covariates
>
>
>
> Hi Bob,
>
>
>
> Thanks for explaining that EM and BAYES methods are a form of naïve pooled
> data analysis for the individual.
>
>
>
> I will make sure I stick to the classic methods when dealing with clinical
> data with time varying covariates such as body mass, post-menstrual age and
> serum creatinine.
>
>
>
> Best wishes,
>
> Nick
>
>
>
> --
>
> Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP
>
> mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72
>
> email: n.holf...@auckland.ac.nz
>
> web: http://holford.fmhs.auckland.ac.nz/
>
>
>
> *From:* Bauer, Robert 
> *Sent:* Tuesday, 14 January 2025 5:34 am
> *To:* Nick Holford ; 'Leonid Gibiansky' <
> lgibian...@quantpharm.com>; Sébastien Bihorel <
> sebastien.biho...@regeneron.com>; nmusers@globomaxnm.com
> *Subject:* RE: [EXTERNAL] Re: [NMusers

RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[kgkowalski58]

I got an email indicating my message below may not have gone through so, I’m 
sending it again.  Apologies if you receive this message twice.  -Ken

 

 

Hi Bob,

 

I agree with the sentiments of Nick’s email below that if EM and Bayes’ methods 
are simply averaging the time-varying covariates and essentially treating them 
as time-invariant values set to the average covariate values, then this would 
be a MAJOR deficiency in how EM and Bayes methods in NONMEM handle time-varying 
covariates.  Afterall, the main reason we investigate time-varying covariates 
is to evaluate whether certain parameters can change over time within a subject 
where the time-varying covariates may help explain some of the within-subject 
variation (e.g., IOV).  If the EM and Bayes’ methods as implemented in NONMEM 
treat the time-varying covariates as time-invariant at the arithmetic mean 
value, then the predictions will not be properly considering these time-varying 
covariates.  However, I suspect that this is not the case, and the EM and 
Bayes’ methods as implemented in NONMEM are actually considering the 
time-varying nature of these covariates and the confusion comes from an 
ambiguous explanation of what NONMEM is doing.  Let me see if I can explain 
what NONMEM is doing, and you can correct me if I’m wrong or further elaborate 
on Nick’s and my concerns.

 

I assume that EM and Bayes methods are actually using the time-varying 
covariates, however, the EM and Bayes’ methods perform centering and/or scaling 
based on the subject-specific arithmetic mean values of the covariates when 
mu-referencing is implemented.  This is to enhance numerical stability when 
estimating the fixed effects associated with time-varying covariates.  Thus, 
the EM and Bayes’ methods are actually using the time-varying values of the 
covariates in the prediction of the responses and the averaging of the 
covariates within a subject is only implemented for MU referencing.  Is my 
understanding correct?

 

Thanks,

 

Ken 

 

 

 

From: owner-nmus...@globomaxnm.com  On Behalf Of 
Nick Holford
Sent: Monday, January 13, 2025 5:49 PM
To: Bauer, Robert ; 'Leonid Gibiansky' 
; Sébastien Bihorel 
; nmusers@globomaxnm.com
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

 

Hi Bob,

 

Thanks for explaining that EM and BAYES methods are a form of naïve pooled data 
analysis for the individual. 

 

I will make sure I stick to the classic methods when dealing with clinical data 
with time varying covariates such as body mass, post-menstrual age and serum 
creatinine.

 

Best wishes,

Nick

 

--

Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP

mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72

email:  <mailto:n.holf...@auckland.ac.nz> n.holf...@auckland.ac.nz

web:  <http://holford.fmhs.auckland.ac.nz/> http://holford.fmhs.auckland.ac.nz/

 

From: Bauer, Robert mailto:robert.ba...@iconplc.com> 
> 
Sent: Tuesday, 14 January 2025 5:34 am
To: Nick Holford mailto:n.holf...@auckland.ac.nz> >; 
'Leonid Gibiansky' mailto:lgibian...@quantpharm.com> >; Sébastien Bihorel 
mailto:sebastien.biho...@regeneron.com> >; 
nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> 
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

 

Hello Nick:

The statement I made pertains only to EM algorithms (ITS, SAEM, IMP), and 
BAYES.  The classic methods (FOCEI, Laplace), do not engage in averaging the 
covariates across records even when thetas are MU referenced, as the classic 
algorithms do not use EM update methods to advance the theta estimates.  

 

Robert J. Bauer, Ph.D.

Senior Director

Pharmacometrics R&D

ICON Early Phase

731 Arbor way, suite 100

Blue Bell, PA 19422  

Office: (215) 616-6428

Mobile: (925) 286-0769

 <mailto:robert.ba...@iconplc.com> robert.ba...@iconplc.com

 <http://www.iconplc.com/> www.iconplc.com

 

From: Nick Holford mailto:n.holf...@auckland.ac.nz> 
> 
Sent: Saturday, January 11, 2025 9:29 PM
To: Bauer, Robert mailto:robert.ba...@iconplc.com> 
>; 'Leonid Gibiansky' mailto:lgibian...@quantpharm.com> >; Sébastien Bihorel 
mailto:sebastien.biho...@regeneron.com> >; 
nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> 
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

 

Hi Bob,

 

I am really puzzled by this statement. I would expect NONMEM to recognize time 
varying covariates provide information about the fixed effects and used the 
time specific value of the covariate to make a prediction.

Averaging the covariate across all the records for a subject seems like a poor 
use of information.

Is your statement saying something special associated with the mu-referenced 
transformation? If so would you please clarify your statement about averaging?

 

Best wishes,

Nick

 

--

Nick Holford, Professor E

RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[kgkowalski58]

Hi Bob,

 

I agree with the sentiments of Nick’s email below that if EM and Bayes’ methods 
are simply averaging the time-varying covariates and essentially treating them 
as time-invariant values set to the average covariate values, then this would 
be a MAJOR deficiency in how EM and Bayes methods in NONMEM handle time-varying 
covariates.  Afterall, the main reason we investigate time-varying covariates 
is to evaluate whether certain parameters can change over time within a subject 
where the time-varying covariates may help explain some of the within-subject 
variation (e.g., IOV).  If the EM and Bayes’ methods as implemented in NONMEM 
treat the time-varying covariates as time-invariant at the arithmetic mean 
value, then the predictions will not be properly considering these time-varying 
covariates.  However, I suspect that this is not the case, and the EM and 
Bayes’ methods as implemented in NONMEM are actually considering the 
time-varying nature of these covariates and the confusion comes from an 
ambiguous explanation of what NONMEM is doing.  Let me see if I can explain 
what NONMEM is doing, and you can correct me if I’m wrong or further elaborate 
on Nick’s and my concerns.

 

I assume that EM and Bayes methods are actually using the time-varying 
covariates, however, the EM and Bayes’ methods perform centering and/or scaling 
based on the subject-specific arithmetic mean values of the covariates when 
mu-referencing is implemented.  This is to enhance numerical stability when 
estimating the fixed effects associated with time-varying covariates.  Thus, 
the EM and Bayes’ methods are actually using the time-varying values of the 
covariates in the prediction of the responses and the averaging of the 
covariates within a subject is only implemented for MU referencing.  Is my 
understanding correct?

 

Thanks,

 

Ken 

 

From: owner-nmus...@globomaxnm.com  On Behalf Of 
Nick Holford
Sent: Monday, January 13, 2025 5:49 PM
To: Bauer, Robert ; 'Leonid Gibiansky' 
; Sébastien Bihorel 
; nmusers@globomaxnm.com
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

 

Hi Bob,

 

Thanks for explaining that EM and BAYES methods are a form of naïve pooled data 
analysis for the individual. 

 

I will make sure I stick to the classic methods when dealing with clinical data 
with time varying covariates such as body mass, post-menstrual age and serum 
creatinine.

 

Best wishes,

Nick

 

--

Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP

mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72

email:  <mailto:n.holf...@auckland.ac.nz> n.holf...@auckland.ac.nz

web:  <http://holford.fmhs.auckland.ac.nz/> http://holford.fmhs.auckland.ac.nz/

 

From: Bauer, Robert mailto:robert.ba...@iconplc.com> 
> 
Sent: Tuesday, 14 January 2025 5:34 am
To: Nick Holford mailto:n.holf...@auckland.ac.nz> >; 
'Leonid Gibiansky' mailto:lgibian...@quantpharm.com> >; Sébastien Bihorel 
mailto:sebastien.biho...@regeneron.com> >; 
nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> 
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

 

Hello Nick:

The statement I made pertains only to EM algorithms (ITS, SAEM, IMP), and 
BAYES.  The classic methods (FOCEI, Laplace), do not engage in averaging the 
covariates across records even when thetas are MU referenced, as the classic 
algorithms do not use EM update methods to advance the theta estimates.  

 

Robert J. Bauer, Ph.D.

Senior Director

Pharmacometrics R&D

ICON Early Phase

731 Arbor way, suite 100

Blue Bell, PA 19422  

Office: (215) 616-6428

Mobile: (925) 286-0769

 <mailto:robert.ba...@iconplc.com> robert.ba...@iconplc.com

 <http://www.iconplc.com/> www.iconplc.com

 

From: Nick Holford mailto:n.holf...@auckland.ac.nz> 
> 
Sent: Saturday, January 11, 2025 9:29 PM
To: Bauer, Robert mailto:robert.ba...@iconplc.com> 
>; 'Leonid Gibiansky' mailto:lgibian...@quantpharm.com> >; Sébastien Bihorel 
mailto:sebastien.biho...@regeneron.com> >; 
nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> 
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

 

Hi Bob,

 

I am really puzzled by this statement. I would expect NONMEM to recognize time 
varying covariates provide information about the fixed effects and used the 
time specific value of the covariate to make a prediction.

Averaging the covariate across all the records for a subject seems like a poor 
use of information.

Is your statement saying something special associated with the mu-referenced 
transformation? If so would you please clarify your statement about averaging?

 

Best wishes,

Nick

 

--

Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP

mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72

email:  <mailto:n.holf...@auckland.ac.nz> n.

RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[Nick Holford]

Hi Bob,

Thanks for explaining that EM and BAYES methods are a form of naïve pooled data 
analysis for the individual.

I will make sure I stick to the classic methods when dealing with clinical data 
with time varying covariates such as body mass, post-menstrual age and serum 
creatinine.

Best wishes,
Nick

--
Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP
mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72
email: n.holf...@auckland.ac.nz<mailto:n.holf...@auckland.ac.nz>
web: http://holford.fmhs.auckland.ac.nz/

From: Bauer, Robert 
Sent: Tuesday, 14 January 2025 5:34 am
To: Nick Holford ; 'Leonid Gibiansky' 
; Sébastien Bihorel 
; nmusers@globomaxnm.com
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hello Nick:
The statement I made pertains only to EM algorithms (ITS, SAEM, IMP), and 
BAYES.  The classic methods (FOCEI, Laplace), do not engage in averaging the 
covariates across records even when thetas are MU referenced, as the classic 
algorithms do not use EM update methods to advance the theta estimates.

Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com>
www.iconplc.com<http://www.iconplc.com/>

From: Nick Holford mailto:n.holf...@auckland.ac.nz>>
Sent: Saturday, January 11, 2025 9:29 PM
To: Bauer, Robert mailto:robert.ba...@iconplc.com>>; 
'Leonid Gibiansky' 
mailto:lgibian...@quantpharm.com>>; Sébastien 
Bihorel 
mailto:sebastien.biho...@regeneron.com>>; 
nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com>
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Bob,

I am really puzzled by this statement. I would expect NONMEM to recognize time 
varying covariates provide information about the fixed effects and used the 
time specific value of the covariate to make a prediction.
Averaging the covariate across all the records for a subject seems like a poor 
use of information.
Is your statement saying something special associated with the mu-referenced 
transformation? If so would you please clarify your statement about averaging?

Best wishes,
Nick

--
Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP
mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72
email: n.holf...@auckland.ac.nz<mailto:n.holf...@auckland.ac.nz>
web: http://holford.fmhs.auckland.ac.nz/

From: owner-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> 
mailto:owner-nmus...@globomaxnm.com>> On Behalf 
Of Bauer, Robert
Sent: Saturday, 11 January 2025 8:32 pm
To: 'Leonid Gibiansky' 
mailto:lgibian...@quantpharm.com>>; Sébastien 
Bihorel 
mailto:sebastien.biho...@regeneron.com>>; 
nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com>
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

If a covariate varies across records within a subject, NONMEM obtains a simple 
average among the records and uses this as the covariate value for that subject.

Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com>
www.iconplc.com<http://www.iconplc.com>

-Original Message-
From: owner-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> 
mailto:owner-nmus...@globomaxnm.com>> On Behalf 
Of Leonid Gibiansky
Sent: Friday, January 10, 2025 5:21 PM
To: Sébastien Bihorel 
mailto:sebastien.biho...@regeneron.com>>; 
nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com>
Subject: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Sébastien,

As you did these experiments, can you share the results: have you seen any 
differences in the fit, parameter estimates, precision, convergence speed 
(number of iteration), and evaluation time for SAEM/IMP (I think, FOCEI does 
not have this restriction of time-independence even if you use Mu referencing, 
so results should be identical or very close).

As code is encrypted, only Bob can answer the question but my understanding is 
that some kind of averaging is used to get time independent value of WT that is 
then used by the SAEM/IMP algorithm for parameter update procedure.

As WT changes slowly and not very significantly, it could be hard to see the 
differences. A more stringent test would be to use time-dependent and strongly 
influential ADA (0/1): how bad is the incorrect version 1 in this case?

Thank you
Leonid

On 1/10/2025 2:56 PM, Sébastien Bihorel wrote:
>
> Happy New Year,
>
> I hope everybody is ready for a great 2025 !
>
> I'll start my message/question by defining 2 different ways of coding
> a simple pow

RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[Bauer, Robert]

Hello Nick:
The statement I made pertains only to EM algorithms (ITS, SAEM, IMP), and 
BAYES.  The classic methods (FOCEI, Laplace), do not engage in averaging the 
covariates across records even when thetas are MU referenced, as the classic 
algorithms do not use EM update methods to advance the theta estimates.

Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com>
www.iconplc.com<http://www.iconplc.com/>

From: Nick Holford 
Sent: Saturday, January 11, 2025 9:29 PM
To: Bauer, Robert ; 'Leonid Gibiansky' 
; Sébastien Bihorel 
; nmusers@globomaxnm.com
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Bob,

I am really puzzled by this statement. I would expect NONMEM to recognize time 
varying covariates provide information about the fixed effects and used the 
time specific value of the covariate to make a prediction.
Averaging the covariate across all the records for a subject seems like a poor 
use of information.
Is your statement saying something special associated with the mu-referenced 
transformation? If so would you please clarify your statement about averaging?

Best wishes,
Nick

--
Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP
mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72
email: n.holf...@auckland.ac.nz<mailto:n.holf...@auckland.ac.nz>
web: http://holford.fmhs.auckland.ac.nz/<http://holford.fmhs.auckland.ac.nz/>

From: owner-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> 
mailto:owner-nmus...@globomaxnm.com>> On Behalf 
Of Bauer, Robert
Sent: Saturday, 11 January 2025 8:32 pm
To: 'Leonid Gibiansky' 
mailto:lgibian...@quantpharm.com>>; Sébastien 
Bihorel 
mailto:sebastien.biho...@regeneron.com>>; 
nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com>
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

If a covariate varies across records within a subject, NONMEM obtains a simple 
average among the records and uses this as the covariate value for that subject.

Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com>
www.iconplc.com<http://www.iconplc.com>

-Original Message-
From: owner-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> 
mailto:owner-nmus...@globomaxnm.com>> On Behalf 
Of Leonid Gibiansky
Sent: Friday, January 10, 2025 5:21 PM
To: Sébastien Bihorel 
mailto:sebastien.biho...@regeneron.com>>; 
nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com>
Subject: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Sébastien,

As you did these experiments, can you share the results: have you seen any 
differences in the fit, parameter estimates, precision, convergence speed 
(number of iteration), and evaluation time for SAEM/IMP (I think, FOCEI does 
not have this restriction of time-independence even if you use Mu referencing, 
so results should be identical or very close).

As code is encrypted, only Bob can answer the question but my understanding is 
that some kind of averaging is used to get time independent value of WT that is 
then used by the SAEM/IMP algorithm for parameter update procedure.

As WT changes slowly and not very significantly, it could be hard to see the 
differences. A more stringent test would be to use time-dependent and strongly 
influential ADA (0/1): how bad is the incorrect version 1 in this case?

Thank you
Leonid

On 1/10/2025 2:56 PM, Sébastien Bihorel wrote:
>
> Happy New Year,
>
> I hope everybody is ready for a great 2025 !
>
> I'll start my message/question by defining 2 different ways of coding
> a simple power relationship between body weigh on clearance.
>
> *
> Coding 1
>
> MU_1 = THETA(1) + THETA(2) * LOG(WGT/70) CL = EXP( MU_1 + ETA(1) )
>
> *
> Coding 2
>
> MU_1 = THETA(1)
> CL = EXP( MU_1 + ETA(1) ) * ( WGT/70 )**THETA(2)
>
> The reference and training materials for NONMEM clearly indicate that
> MU variables should be time invariant within occasions and recommend
> using coding 2 when body weight is time varying. Nevertheless, it is
> possible for an analyst to use coding 1. As far as I can tell from
> some limited testing, this is not a "fatal" error. Either with FOCE(I)
> or SAEM/IMP, NONMEM reports a warning but performs the model
> optimization. The table outputs also report CL as a time varying
> variable changing as body weight changes.
>
> So my questions are the following: when coding 1 is used and body
> weight is time varying, what is NONME

RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[Nick Holford]

Hi Bob,

I am really puzzled by this statement. I would expect NONMEM to recognize time 
varying covariates provide information about the fixed effects and used the 
time specific value of the covariate to make a prediction.
Averaging the covariate across all the records for a subject seems like a poor 
use of information.
Is your statement saying something special associated with the mu-referenced 
transformation? If so would you please clarify your statement about averaging?

Best wishes,
Nick

--
Nick Holford, Professor Emeritus Clinical Pharmacology, MBChB, FRACP
mobile: NZ+64(21) 46 23 53 ; FR+33(6) 62 32 46 72
email: n.holf...@auckland.ac.nz<mailto:n.holf...@auckland.ac.nz>
web: http://holford.fmhs.auckland.ac.nz/

From: owner-nmus...@globomaxnm.com  On Behalf Of 
Bauer, Robert
Sent: Saturday, 11 January 2025 8:32 pm
To: 'Leonid Gibiansky' ; Sébastien Bihorel 
; nmusers@globomaxnm.com
Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

If a covariate varies across records within a subject, NONMEM obtains a simple 
average among the records and uses this as the covariate value for that subject.

Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com>
www.iconplc.com<http://www.iconplc.com>

-Original Message-
From: owner-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> 
mailto:owner-nmus...@globomaxnm.com>> On Behalf 
Of Leonid Gibiansky
Sent: Friday, January 10, 2025 5:21 PM
To: Sébastien Bihorel 
mailto:sebastien.biho...@regeneron.com>>; 
nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com>
Subject: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Sébastien,

As you did these experiments, can you share the results: have you seen any 
differences in the fit, parameter estimates, precision, convergence speed 
(number of iteration), and evaluation time for SAEM/IMP (I think, FOCEI does 
not have this restriction of time-independence even if you use Mu referencing, 
so results should be identical or very close).

As code is encrypted, only Bob can answer the question but my understanding is 
that some kind of averaging is used to get time independent value of WT that is 
then used by the SAEM/IMP algorithm for parameter update procedure.

As WT changes slowly and not very significantly, it could be hard to see the 
differences. A more stringent test would be to use time-dependent and strongly 
influential ADA (0/1): how bad is the incorrect version 1 in this case?

Thank you
Leonid

On 1/10/2025 2:56 PM, Sébastien Bihorel wrote:
>
> Happy New Year,
>
> I hope everybody is ready for a great 2025 !
>
> I'll start my message/question by defining 2 different ways of coding
> a simple power relationship between body weigh on clearance.
>
> *
> Coding 1
>
> MU_1 = THETA(1) + THETA(2) * LOG(WGT/70) CL = EXP( MU_1 + ETA(1) )
>
> *
> Coding 2
>
> MU_1 = THETA(1)
> CL = EXP( MU_1 + ETA(1) ) * ( WGT/70 )**THETA(2)
>
> The reference and training materials for NONMEM clearly indicate that
> MU variables should be time invariant within occasions and recommend
> using coding 2 when body weight is time varying. Nevertheless, it is
> possible for an analyst to use coding 1. As far as I can tell from
> some limited testing, this is not a "fatal" error. Either with FOCE(I)
> or SAEM/IMP, NONMEM reports a warning but performs the model
> optimization. The table outputs also report CL as a time varying
> variable changing as body weight changes.
>
> So my questions are the following: when coding 1 is used and body
> weight is time varying, what is NONMEM actually doing during model
> optimization? Does NONMEM internally create occasions to break the
> records by interval of constant body weight and constant MU1?
> Alternatively, does NONMEM internally calculate an average of MU1?
> Something entirely different? What's the risk taken by an analyst when
> using coding 1 versus coding 2?
>
> Thank you in advance for you input
>
>
> __
> Sébastien Bihorel
> Director, Quantitative Pharmacology
> +1 914-648-9581
> sebastien.biho...@regeneron.com<mailto:sebastien.biho...@regeneron.com>
>
>
> Regeneron - Internal
>
> 
> This e-mail and any attachment hereto, is intended only for use by the
> addressee(s) named above and may contain legally privileged and/or
> confidential information. If you are not the intended recipient of
> this e-mail, any dissemination, distribution or copying of this email,
> or any attachment hereto, is strictly prohibited. If you receive this
> email in 

Re: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[Leonid Gibiansky]

Is this also true for the FOCEI when MU-referencing is present, or only 
for SAEM/IMP/etc "new" methods?

Thank you
Leonid

On 1/11/2025 2:31 AM, Bauer, Robert wrote:
If a covariate varies across records within a subject, NONMEM obtains a 
simple average among the records and uses this as the covariate value 
for that subject.


Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com 
www.iconplc.com 

-Original Message-
From: owner-nmus...@globomaxnm.com  
 On Behalf Of Leonid Gibiansky

Sent: Friday, January 10, 2025 5:21 PM
To: Sébastien Bihorel ; 
nmusers@globomaxnm.com 

Subject: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Sébastien,

As you did these experiments, can you share the results: have you seen 
any differences in the fit, parameter estimates, precision, convergence 
speed (number of iteration), and evaluation time for SAEM/IMP (I think, 
FOCEI does not have this restriction of time-independence even if you 
use Mu referencing, so results should be identical or very close).


As code is encrypted, only Bob can answer the question but my 
understanding is that some kind of averaging is used to get time 
independent value of WT that is then used by the SAEM/IMP algorithm for 
parameter update procedure.


As WT changes slowly and not very significantly, it could be hard to see 
the differences. A more stringent test would be to use time-dependent 
and strongly influential ADA (0/1): how bad is the incorrect version 1 
in this case?


Thank you
Leonid

On 1/10/2025 2:56 PM, Sébastien Bihorel wrote:
 >
 > Happy New Year,
 >
 > I hope everybody is ready for a great 2025 !
 >
 > I'll start my message/question by defining 2 different ways of coding
 > a simple power relationship between body weigh on clearance.
 >
 > *
 > Coding 1
 >
 > MU_1 = THETA(1) + THETA(2) * LOG(WGT/70) CL = EXP( MU_1 + ETA(1) )
 >
 > *
 > Coding 2
 >
 > MU_1 = THETA(1)
 > CL = EXP( MU_1 + ETA(1) ) * ( WGT/70 )**THETA(2)
 >
 > The reference and training materials for NONMEM clearly indicate that
 > MU variables should be time invariant within occasions and recommend
 > using coding 2 when body weight is time varying. Nevertheless, it is
 > possible for an analyst to use coding 1. As far as I can tell from
 > some limited testing, this is not a "fatal" error. Either with FOCE(I)
 > or SAEM/IMP, NONMEM reports a warning but performs the model
 > optimization. The table outputs also report CL as a time varying
 > variable changing as body weight changes.
 >
 > So my questions are the following: when coding 1 is used and body
 > weight is time varying, what is NONMEM actually doing during model
 > optimization? Does NONMEM internally create occasions to break the
 > records by interval of constant body weight and constant MU1?
 > Alternatively, does NONMEM internally calculate an average of MU1?
 > Something entirely different? What's the risk taken by an analyst when
 > using coding 1 versus coding 2?
 >
 > Thank you in advance for you input
 >
 >
 > __
 > Sébastien Bihorel
 > Director, Quantitative Pharmacology
 > +1 914-648-9581
 > sebastien.biho...@regeneron.com 
 >
 >
 > Regeneron - Internal
 >
 > 
 > This e-mail and any attachment hereto, is intended only for use by the
 > addressee(s) named above and may contain legally privileged and/or
 > confidential information. If you are not the intended recipient of
 > this e-mail, any dissemination, distribution or copying of this email,
 > or any attachment hereto, is strictly prohibited. If you receive this
 > email in error please immediately notify me by return electronic mail
 > and permanently delete this email and any attachment hereto, any copy
 > of this e-mail and of any such attachment, and any printout thereof.
 > Finally, please note that only authorized representatives of Regeneron
 > Pharmaceuticals, Inc. have the power and authority to enter into
 > business dealings with any third party.
 > 


ICON plc made the following annotations.
--
This e-mail transmission may contain confidential or legally privileged 
information that is intended only for the individual or entity named in 
the e-mail address. If you are not the intended recipient, you are 
hereby notified that any disclosure, copying, distribution, or reliance 
upon the contents of this e-mail is strictly prohibited. If you have 
received this e-mail transmission in error, please reply to the sender, 
so that ICON plc can arrange for proper delivery, and then please de

Re: [External] Re: [NMusers] MU referencing and time-varying covariates

[Sébastien Bihorel]

Hi Leonid,

I would not use my quite limited tests for any conclusions with respect to 
precision or convergence properties of coding #1. NONMEM definitively reported 
warnings with both FOCE(I) and SAEM/IMP runs and both runs proceeded to 
optimize the model parameters. The paths of optimization and number of 
iterations were different. The final estimates differed from the runs that used 
coding #2. The fit was not terribly different across runs but, as you said, 
more impact may be observed when the covariates change more drastically.

Based upon Bob's response, it is interesting to think that individuals with 
time varying discrete covariates would not be considered as being part of one 
category or another, under coding #1, but as weighted average of each category 
they can belong to...

Sebastien


Regeneron - Internal


From: Leonid Gibiansky 
Sent: Friday, January 10, 2025 8:21 PM
To: Sébastien Bihorel ; nmusers@globomaxnm.com 

Subject: [External] Re: [NMusers] MU referencing and time-varying covariates

Hi Sébastien, As you did these experiments, can you share the results: have you 
seen any differences in the fit, parameter estimates, precision, convergence 
speed (number of iteration), and evaluation time for SAEM/IMP (I think, FOCEI 
does not

EXTERNAL MESSAGE
_

Hi Sébastien,

As you did these experiments, can you share the results: have you seen
any differences in the fit, parameter estimates, precision, convergence
speed (number of iteration), and evaluation time for SAEM/IMP (I think,
FOCEI does not have this restriction of time-independence even if you
use Mu referencing, so results should be identical or very close).

As code is encrypted, only Bob can answer the question but my
understanding is that some kind of averaging is used to get time
independent value of WT that is then used by the SAEM/IMP algorithm for
parameter update procedure.

As WT changes slowly and not very significantly, it could be hard to see
the differences. A more stringent test would be to use time-dependent
and strongly influential ADA (0/1): how bad is the incorrect version 1
in this case?

Thank you
Leonid

On 1/10/2025 2:56 PM, Sébastien Bihorel wrote:
>
> Happy New Year,
>
> I hope everybody is ready for a great 2025 !
>
> I'll start my message/question by defining 2 different ways of coding a
> simple power relationship between body weigh on clearance.
>
>   *
> Coding 1
>
> MU_1 = THETA(1) + THETA(2) * LOG(WGT/70)
> CL = EXP( MU_1 + ETA(1) )
>
>   *
> Coding 2
>
> MU_1 = THETA(1)
> CL = EXP( MU_1 + ETA(1) ) * ( WGT/70 )**THETA(2)
>
> The reference and training materials for NONMEM clearly indicate that MU
> variables should be time invariant within occasions and recommend using
> coding 2 when body weight is time varying. Nevertheless, it is possible
> for an analyst to use coding 1. As far as I can tell from some limited
> testing, this is not a "fatal" error. Either with FOCE(I) or SAEM/IMP,
> NONMEM reports a warning but performs the model optimization. The table
> outputs also report CL as a time varying variable changing as body
> weight changes.
>
> So my questions are the following: when coding 1 is used and body weight
> is time varying, what is NONMEM actually doing during model
> optimization? Does NONMEM internally create occasions to break the
> records by interval of constant body weight and constant MU1?
> Alternatively, does NONMEM internally calculate an average of MU1?
> Something entirely different? What's the risk taken by an analyst when
> using coding 1 versus coding 2?
>
> Thank you in advance for you input
>
>
> __
> Sébastien Bihorel
> Director, Quantitative Pharmacology
> +1 914-648-9581
> sebastien.biho...@regeneron.com
>
>
> Regeneron - Internal
>
> 
> This e-mail and any attachment hereto, is intended only for use by the
> addressee(s) named above and may contain legally privileged and/or
> confidential information. If you are not the intended recipient of this
> e-mail, any dissemination, distribution or copying of this email, or any
> attachment hereto, is strictly prohibited. If you receive this email in
> error please immediately notify me by return electronic mail and
> permanently delete this email and any attachment hereto, any copy of
> this e-mail and of any such attachment, and any printout thereof.
> Finally, please note that only authorized representatives of Regeneron
> Pharmaceuticals, Inc. have the power and authority to enter into
> business dealings with any third party.
> 


 
This e-mail and any attachment hereto, is intended only for use by the 
addressee(s) named above and may contain legally privileged and/or confidential 
information. If you are not the i

Re: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[Sébastien Bihorel]

Thank you for the clarification. Much appreciated.


Regeneron - Internal


From: Bauer, Robert 
Sent: Saturday, January 11, 2025 2:31 AM
To: 'Leonid Gibiansky' ; Sébastien Bihorel 
; nmusers@globomaxnm.com 

Subject: RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

If a covariate varies across records within a subject, NONMEM obtains a simple 
average among the records and uses this as the covariate value for that 
subject. Robert J. Bauer, Ph. D. Senior Director Pharmacometrics R&D ICON Early 
Phase 731

If a covariate varies across records within a subject, NONMEM obtains a simple 
average among the records and uses this as the covariate value for that subject.

Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com>
www.iconplc.com<https://urldefense.com/v3/__http://www.iconplc.com__;!!ODpDvJZr5w!GNRSQC0vmVcClv1zMJgsNsCcNeoMkxqOaEMlRn6_-jHqy6jald8QxKYV1I6AUPX3dritCEex3_fXtP0HHRqN6YOFPjqy8iI$>

-Original Message-
From: owner-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> 
 On Behalf Of Leonid Gibiansky
Sent: Friday, January 10, 2025 5:21 PM
To: Sébastien Bihorel ; 
nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com>
Subject: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Sébastien,

As you did these experiments, can you share the results: have you seen any 
differences in the fit, parameter estimates, precision, convergence speed 
(number of iteration), and evaluation time for SAEM/IMP (I think, FOCEI does 
not have this restriction of time-independence even if you use Mu referencing, 
so results should be identical or very close).

As code is encrypted, only Bob can answer the question but my understanding is 
that some kind of averaging is used to get time independent value of WT that is 
then used by the SAEM/IMP algorithm for parameter update procedure.

As WT changes slowly and not very significantly, it could be hard to see the 
differences. A more stringent test would be to use time-dependent and strongly 
influential ADA (0/1): how bad is the incorrect version 1 in this case?

Thank you
Leonid

On 1/10/2025 2:56 PM, Sébastien Bihorel wrote:
>
> Happy New Year,
>
> I hope everybody is ready for a great 2025 !
>
> I'll start my message/question by defining 2 different ways of coding
> a simple power relationship between body weigh on clearance.
>
> *
> Coding 1
>
> MU_1 = THETA(1) + THETA(2) * LOG(WGT/70) CL = EXP( MU_1 + ETA(1) )
>
> *
> Coding 2
>
> MU_1 = THETA(1)
> CL = EXP( MU_1 + ETA(1) ) * ( WGT/70 )**THETA(2)
>
> The reference and training materials for NONMEM clearly indicate that
> MU variables should be time invariant within occasions and recommend
> using coding 2 when body weight is time varying. Nevertheless, it is
> possible for an analyst to use coding 1. As far as I can tell from
> some limited testing, this is not a "fatal" error. Either with FOCE(I)
> or SAEM/IMP, NONMEM reports a warning but performs the model
> optimization. The table outputs also report CL as a time varying
> variable changing as body weight changes.
>
> So my questions are the following: when coding 1 is used and body
> weight is time varying, what is NONMEM actually doing during model
> optimization? Does NONMEM internally create occasions to break the
> records by interval of constant body weight and constant MU1?
> Alternatively, does NONMEM internally calculate an average of MU1?
> Something entirely different? What's the risk taken by an analyst when
> using coding 1 versus coding 2?
>
> Thank you in advance for you input
>
>
> __
> Sébastien Bihorel
> Director, Quantitative Pharmacology
> +1 914-648-9581
> sebastien.biho...@regeneron.com<mailto:sebastien.biho...@regeneron.com>
>
>
> Regeneron - Internal
>
> 
> This e-mail and any attachment hereto, is intended only for use by the
> addressee(s) named above and may contain legally privileged and/or
> confidential information. If you are not the intended recipient of
> this e-mail, any dissemination, distribution or copying of this email,
> or any attachment hereto, is strictly prohibited. If you receive this
> email in error please immediately notify me by return electronic mail
> and permanently delete this email and any attachment hereto, any copy
> of this e-mail and of any such attachment, and any printout thereof.
> Finally, please note that only authorized representatives of Regeneron
> Pharmaceuticals, Inc. have the power and authority to enter into
> business deal

RE: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

[Bauer, Robert]

If a covariate varies across records within a subject, NONMEM obtains a simple 
average among the records and uses this as the covariate value for that subject.

Robert J. Bauer, Ph.D.
Senior Director
Pharmacometrics R&D
ICON Early Phase
731 Arbor way, suite 100
Blue Bell, PA 19422  
Office: (215) 616-6428
Mobile: (925) 286-0769
robert.ba...@iconplc.com
www.iconplc.com

-Original Message-
From: owner-nmus...@globomaxnm.com  On Behalf Of 
Leonid Gibiansky
Sent: Friday, January 10, 2025 5:21 PM
To: Sébastien Bihorel ; nmusers@globomaxnm.com
Subject: [EXTERNAL] Re: [NMusers] MU referencing and time-varying covariates

Hi Sébastien,

As you did these experiments, can you share the results: have you seen any 
differences in the fit, parameter estimates, precision, convergence speed 
(number of iteration), and evaluation time for SAEM/IMP (I think, FOCEI does 
not have this restriction of time-independence even if you use Mu referencing, 
so results should be identical or very close).

As code is encrypted, only Bob can answer the question but my understanding is 
that some kind of averaging is used to get time independent value of WT that is 
then used by the SAEM/IMP algorithm for parameter update procedure.

As WT changes slowly and not very significantly, it could be hard to see the 
differences. A more stringent test would be to use time-dependent and strongly 
influential ADA (0/1): how bad is the incorrect version 1 in this case?

Thank you
Leonid

On 1/10/2025 2:56 PM, Sébastien Bihorel wrote:
> 
> Happy New Year,
> 
> I hope everybody is ready for a great 2025 !
> 
> I'll start my message/question by defining 2 different ways of coding 
> a simple power relationship between body weigh on clearance.
> 
>   *
> Coding 1
> 
> MU_1 = THETA(1) + THETA(2) * LOG(WGT/70) CL = EXP( MU_1 + ETA(1) )
> 
>   *
> Coding 2
> 
> MU_1 = THETA(1)
> CL = EXP( MU_1 + ETA(1) ) * ( WGT/70 )**THETA(2)
> 
> The reference and training materials for NONMEM clearly indicate that 
> MU variables should be time invariant within occasions and recommend 
> using coding 2 when body weight is time varying. Nevertheless, it is 
> possible for an analyst to use coding 1. As far as I can tell from 
> some limited testing, this is not a "fatal" error. Either with FOCE(I) 
> or SAEM/IMP, NONMEM reports a warning but performs the model 
> optimization. The table outputs also report CL as a time varying 
> variable changing as body weight changes.
> 
> So my questions are the following: when coding 1 is used and body 
> weight is time varying, what is NONMEM actually doing during model 
> optimization? Does NONMEM internally create occasions to break the 
> records by interval of constant body weight and constant MU1?
> Alternatively, does NONMEM internally calculate an average of MU1? 
> Something entirely different? What's the risk taken by an analyst when 
> using coding 1 versus coding 2?
> 
> Thank you in advance for you input
> 
> 
> __
> Sébastien Bihorel
> Director, Quantitative Pharmacology
> +1 914-648-9581
> sebastien.biho...@regeneron.com
> 
> 
> Regeneron - Internal
> 
> 
> This e-mail and any attachment hereto, is intended only for use by the
> addressee(s) named above and may contain legally privileged and/or 
> confidential information. If you are not the intended recipient of 
> this e-mail, any dissemination, distribution or copying of this email, 
> or any attachment hereto, is strictly prohibited. If you receive this 
> email in error please immediately notify me by return electronic mail 
> and permanently delete this email and any attachment hereto, any copy 
> of this e-mail and of any such attachment, and any printout thereof.
> Finally, please note that only authorized representatives of Regeneron 
> Pharmaceuticals, Inc. have the power and authority to enter into 
> business dealings with any third party.
> 

ICON plc made the following annotations.
--
This e-mail transmission may contain confidential or legally privileged 
information that is intended only for the individual or entity named in the 
e-mail address. If you
are not the intended recipient, you are hereby notified that any disclosure, 
copying, distribution, or reliance upon the contents of this e-mail is strictly 
prohibited. If
you have received this e-mail transmission in error, please reply to the 
sender, so that ICON plc can arrange for proper delivery, and then please 
delete the message.

Thank You,

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