Dear all:
when we model parent and metabolite simultaneously, can we model fraction of conversion of parent drug to metabolite without any assumptions to see what fraction is based on the data only? I am thinking, at this point, estimation of volume of distribution of metabolite is V/F without any assumptions, F is fraction of conversion. we don't need know volume of distribution of metabolite exactly because we want know fraction of conversion first. Once we know the fraction of conversion(F) after this step, we can model the volume of distribution of metabolite(V) next exactly. Any instructions will be highly appreciated.
Zhen
Quoting Stephen Duffull <[EMAIL PROTECTED]>:
> Mike
>
> There is no requirement that the number of occasions is the same for all
> individuals in the study or for all studies in your dataset. So I don't see
> why you want the number of occasions to be the same for all ID numbers.
>
> On a similar note - I believe that the division of within subject
> variability (WSV) into between occasion (BOV) and within occasion (WOV),
> although helpful and somewhat intuitive, is also arbitrary. Indeed if you
> took an asymptotic example where you made the occasion to equal to (say) 1
> year and your study had samples over 2 years then BOV would turn out to be
> rather inconsequential since the parameter values would be averaged over
> each occasion and the random variability between occasions is likely to be
> quite small.
>
> So, if WSV = BOV + WOV then as the occasion duration tends to infinity then
> WSV --> WOV and BOV --> 0. In converse, as occasion duration tends to zero
> then WSV --> BOV and WOV --> 0 [assuming you can accurately estimate BOV].
> The value of BOV is therefore design specific and hence any benefit from
> interpretation of BOV can only be gained if the occasion is set a priori to
> a clinically or biologically meaningful interval (e.g. 1 dose interval).
> [i.e. To be (perhaps inappropriately) provocative, if large estimated BOV is
> bad for your drug then all you need to do is make the occasion duration to
> be very long :-)]
>
> Just some thoughts.
>
> Steve
> --
> Professor Stephen Duffull
> Chair of Clinical Pharmacy
> School of Pharmacy
> University of Otago
> PO Box 913 Dunedin
> New Zealand
> E: [EMAIL PROTECTED]
> P: +64 3 479 5044
> F: +64 3 479 7034
>
> Design software: www.winpopt.com
>
> > -----Original Message-----
Quoted reply history
> > From: [EMAIL PROTECTED]
> > [mailto:[EMAIL PROTECTED] On Behalf Of
> > [EMAIL PROTECTED]
> > Sent: Thursday, 23 August 2007 7:04 a.m.
> > To: [email protected]
> > Subject: [NMusers] Assigning occasions to sparse data
> >
> > Hi everyone;
> >
> > I'd like to get some feed-back from the group about how to
> > assign occasions for the estimation of inter-occasional
> > variability. The situation is this: I have a large Phase 1
> > dataset (both single dose and multiple dose) where many
> > samples were taken per subject, and "occasion" is readily
> > assigned by using period. The maximum number of occasions in
> > these data is equal to 4. However, I also have a dataset from
> > a Phase 3 study where sparse samples were taken. As you would
> > expect, the amount of data from patient to patient is quite
> > variable, with some subjects having as few as 2 samples where
> > others have as many as 8. Also, the times (relative to
> > dosing) are quite variable. What I would like to do is to
> > combine these two datasets, and keep the same number of
> > occasions that are in the rich dataset by grouping these
> > sparse samples by time relative to the first dose. For
> > example (and this is arbitrary) I could define Occasion 1 as
> > including any sample taken between 0-48 hours, Occasion 2 as
> > including any sample between 48-72 hrs, etc. up to Occasion 4.
> >
> > Does anyone see a problem with this? Or, do you have a better idea?
> >
> > Many thanks for your time,
> >
> > Mike Fossler
> > GSK