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

From: Bauer, Robert <[email protected]> Sent: Tuesday, 14 January 2025 5:34 am To: Nick Holford <[email protected]>; 'Leonid Gibiansky' <[email protected]>; Sébastien Bihorel <[email protected]>; [email protected] 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 [email protected]<mailto:[email protected]> http://www.iconplc.com/ From: Nick Holford <[email protected]<mailto:[email protected]>> Sent: Saturday, January 11, 2025 9:29 PM To: Bauer, Robert <[email protected]<mailto:[email protected]>>; 'Leonid Gibiansky' <[email protected]<mailto:[email protected]>>; Sébastien Bihorel <[email protected]<mailto:[email protected]>>; [email protected]<mailto:[email protected]> 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: [email protected]<mailto:[email protected]> web: http://holford.fmhs.auckland.ac.nz/ From: [email protected]<mailto:[email protected]> <[email protected]<mailto:[email protected]>> On Behalf Of Bauer, Robert Sent: Saturday, 11 January 2025 8:32 pm To: 'Leonid Gibiansky' <[email protected]<mailto:[email protected]>>; Sébastien Bihorel <[email protected]<mailto:[email protected]>>; [email protected]<mailto:[email protected]> 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 [email protected]<mailto:[email protected]> http://www.iconplc.com -----Original Message----- From: [email protected]<mailto:[email protected]> <[email protected]<mailto:[email protected]>> On Behalf Of Leonid Gibiansky Sent: Friday, January 10, 2025 5:21 PM To: Sébastien Bihorel <[email protected]<mailto:[email protected]>>; [email protected]<mailto:[email protected]> 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 > [email protected]<mailto:[email protected]> > > > Regeneron - Internal > > ********************************************************************