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 <owner-nmus...@globomaxnm.com> On Behalf Of Nick Holford Sent: Monday, January 13, 2025 5:49 PM To: Bauer, Robert <robert.ba...@iconplc.com>; '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: <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 <robert.ba...@iconplc.com <mailto:robert.ba...@iconplc.com> > Sent: Tuesday, 14 January 2025 5:34 am To: Nick Holford <n.holf...@auckland.ac.nz <mailto:n.holf...@auckland.ac.nz> >; 'Leonid Gibiansky' <lgibian...@quantpharm.com <mailto:lgibian...@quantpharm.com> >; Sébastien Bihorel <sebastien.biho...@regeneron.com <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 <n.holf...@auckland.ac.nz <mailto:n.holf...@auckland.ac.nz> > Sent: Saturday, January 11, 2025 9:29 PM To: Bauer, Robert <robert.ba...@iconplc.com <mailto:robert.ba...@iconplc.com> >; 'Leonid Gibiansky' <lgibian...@quantpharm.com <mailto:lgibian...@quantpharm.com> >; Sébastien Bihorel <sebastien.biho...@regeneron.com <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.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> <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' <lgibian...@quantpharm.com <mailto:lgibian...@quantpharm.com> >; Sébastien Bihorel <sebastien.biho...@regeneron.com <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> <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 <sebastien.biho...@regeneron.com <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 > > ******************************************************************** 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. 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