Re: More Levels of Random Effects

> -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > On Behalf Of Leonid Gibiansky > Sent: Friday, October 17, 2008 10:35 AM > To: [EMAIL PROTECTED] > Cc: nmusers; Nick Holford > Subject: Re: [NMusers] More Levels of Random Effects > > The next level of random effects can be introduced in NONMEM using PRIOR > > subroutine. It existed as undocumented feature even in Nonmem V. Now (in > > Nonmem VI) it is official (see help). > Leonid > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > [EMAIL PROTECTED] wrote: > > > I suppose it really comes down to what you are going to do with the model. Many times I have checked the SAME assumption when modeling inter-occasional variability, and found that sometimes, removing it > > does > > > indeed improve the fit significantly. In almost every case I've retained it (despite the better fit) for the exact reasons Leonid > > cites: > > > it makes your model completely data-dependent. I suppose if the model was meant as a description or summary of the data, then it would not matter, but I make all of my models work for a living... > > > > There is a related topic which I'd be interested in hearing from the group about. Many times, we take several Phase 1 studies and put them together in order to develop a population model early in development. I've learned through experience to be careful when doing this, because > > > often, one or more studies will appear to have a different mean > > response > > > for some parameter, e.g., CL or V2. Of course, you can introduce study > > > as a covariate, but this intrduces the same problem as above; in a simulation context, which CL value is correct? There is a work-around for this (use both values) but this doubles the number of simulations you have to do, and from a scientific stand-point it is not very satisfying. What we need is another level of random effects at the > > STUDY > > > level, similar to what is routinely done when performing hierarchical modeling in something like WinBUGS. I'd love to see this feature in a future version of NONMEM. > > > > *"Leonid Gibiansky" <[EMAIL PROTECTED]>* > > Sent by: [EMAIL PROTECTED] > > > > 17-Oct-2008 09:30 > > > > To > > > > "Nick Holford" <[EMAIL PROTECTED]> > > cc > > "nmusers" <[email protected]> > > Subject > > Re: [NMusers] More Levels of Random Effects > > > > Nick, > > > > This is exactly what I meant. If you have a model for English, Irish > > and > > > Welsh, you may at least extrapolate it to Australians and New > > Zealanders > > > (of British descent :) ). With occasion treated as non-ordered > > categorical covariate, you cannot extrapolate the model at all because > > time cannot be repeated, so your covariate (occasion) will have > > different value (level) at any future trial. > > > > Leonid > > > > -------------------------------------- > > Leonid Gibiansky, Ph.D. > > President, QuantPharm LLC > > web: www.quantpharm.com > > e-mail: LGibiansky at quantpharm.com > > tel: (301) 767 5566 > > > > Nick Holford wrote: > > > Leonid, > > > > > > I dont understand what you mean by "we lose predictive power of the > > > model: we do not know what will be > > > the variability on the next occasion.". > > > > > > Or are you concerned about the situation where you have say 3 > > occasions > > > > and the IOV seems to be different on each occasion but you now want > > to > > > > predict the IOV for a future study on the 4th occasion? > > > > > > I agree it is hard to extrapolate to future occasions but this > > seems to > > > > be just like any other non-ordered categorical covariate - e.g. if > > we > > > > see differences between English, Irish and Welsh what difference > > would > > > > you expect for Russians? :-) > > > > > > Nick > > > > > > > > > Leonid Gibiansky wrote: > > >> Hi Xia, Nick > > >> Technically, one can use different variances on different > > occasions but > > > >> then we loose predictive power of the model: we do not know what > > will be > > > >> the variability on the next occasion. One can use > > occasion-dependent IOV > > > >> variance to check for trends (for example, to investigate the time > > >> dependence of the IOV variability, or to check whether the first > > >> occasion (e.g., after the first dose of a long-term study) is for > > some > > > >> reasons different from the others) but the final model should have > > some > > > >> condition that specifies the relations of IOV variances at > > different > > > >> occasion (SAME being the simplest, most reasonable and the > > most-often > > > >> used option). > > >> > > >> Thanks > > >> Leonid > > >> > > >> -------------------------------------- > > >> Leonid Gibiansky, Ph.D. > > >> President, QuantPharm LLC > > >> web: www.quantpharm.com > > >> e-mail: LGibiansky at quantpharm.com > > >> tel: (301) 767 5566 > > >> > > >> > > >> > > >> > > >> Nick Holford wrote: > > >>> Xia, > > >>> > > >>> There is no requirement to use the SAME option. However, it is a > > >>> reasonable model for IOV that it has the same variability on each > > >>> occasion. > > >>> > > >>> If you dont use the SAME option then you just need to estimate an > > >>> extra OMEGA parameter for each occasion you dont use SAME. You > > can > > > >>> test if the SAME assumption is supported by your data or not by > > >>> comparing models with and without SAME. > > >>> > > >>> Nick > > >>> > > >>> PS Your computer clock seems to be more than 2 years out of date. > > >>> Your email claimed it was sent in 17 Jan 2006. > > >>> > > >>> Xia Li wrote: > > >>>> Dear All, > > >>>> Do we have to assume the variability between all occasions are > > the > > > >>>> same when > > >>>> we estimate IOV? What will happen if I don't use the 'same' > > >>>> constrain in the > > >>>> $OMEGA BLOCK statement? Any input will be appreciated. > > >>>> > > >>>> Best, > > >>>> > > >>>> Xia Li > > >>>> > > >>>> -----Original Message----- > > >>>> From: [EMAIL PROTECTED] > > >>>> [mailto:[EMAIL PROTECTED] On > > >>>> Behalf Of Johan Wallin > > >>>> Sent: Wednesday, October 15, 2008 9:17 AM > > >>>> To: [email protected] > > >>>> Subject: RE: [NMusers] More Levels of Random Effects > > >>>> > > >>>> Bill, > > >>>> Is it really an eta you want, or is this rather solved by > > different > > > >>>> error > > >>>> models for the different machines? > > >>>> > > >>>> If still want etas, one way would be to model in the same way as > > >>>> IOV. In the > > >>>> case of intermachine-variability you would have to assume the > > >>>> variability > > >>>> between all machines are the same... Or would you rather assume > > >>>> interindividual variability is different with > > >>>> different machine, and you then would want one eta for TH(X) for > > every > > > >>>> machine...? It depends on what you mean by different slope every > > day, > > > >>>> regarding on what your experiments like, but calibration > > differences > > > >>>> should > > >>>> perhaps be taken care of by looking into your error model, eta > > on > > > >>>> epsilon > > >>>> for starters... > > >>>> > > >>>> Without knowing your structure of data, a short example of > > IOV-like > > > >>>> variability would be: > > >>>> > > >>>> MA1=0 > > >>>> MA2=0 > > >>>> IF(MACH=1)MA1=1 > > >>>> IF(MACH=2)MA2=1 > > >>>> ;Intermachine variability: > > >>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z) > > >>>> > > >>>> PAR= TH(X) *EXP(ETA(X)+ETAM) > > >>>> > > >>>> $OMEGA value1 > > >>>> $OMEGA BLOCK(1) value2 > > >>>> $OMEGA BLOCK(1) same > > >>>> > > >>>> /Johan > > >>>> > > >>>> > > >>>> _________________________________________ > > >>>> Johan Wallin, M.Sci./Ph.D.-student > > >>>> Pharmacometrics Group > > >>>> Div. of Pharmacokinetics and Drug therapy > > >>>> Uppsala University > > >>>> _________________________________________ > > >>>> > > >>>> > > >>>> -----Original Message----- > > >>>> From: [EMAIL PROTECTED] > > >>>> [mailto:[EMAIL PROTECTED] On > > >>>> Behalf Of Denney, William S. > > >>>> Sent: den 15 oktober 2008 14:39 > > >>>> To: [email protected] > > >>>> Subject: [NMusers] More Levels of Random Effects > > >>>> > > >>>> Hello, > > >>>> > > >>>> I'm trying to build a model where I need to have ETAs generated > > on > > > >>>> separately for the ID and another variable (MACH). What I have > > is > > > a PD > > >>>> experiment that was run on several different machines (MACH). > > Each > > > >>>> machine appears to have a different slope per day and a > > different > > > >>>> calibration. I still need to keep some ETAs on the ID column, > > so I > > > >>>> can't just assign MACH=ID. > > >>>> > > >>>> I've heard that there are ways to do similar to this, but I have > > been > > > >>>> unable to find examples of how to set etas to key off of > > different > > > >>>> columns. > > >>>> > > >>>> Thanks, > > >>>> > > >>>> Bill > > Notice: This e-mail message, together with any attachments, contains > information of Merck & Co., Inc. 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