Re: estimating Ka from dataset combining rich sample study and sparse sampling study
Dear all,
thanks to Mats suggestion, using full cov matrix did assign Eta more
reasonably to Ka, with not very precised estimates
another suggestion is that, there may be some underline difference in the
structure model between sparse MD data, and rich SD -- by visual inspection of
sparse MD data, I really can't think it can support more complext model itself
with 2-3 datapoints per subject in each study period (total of 2), with the
sampling the same across subjects.
another suggestion is that to model study individually, both Ka and IIV on
Ka estimated from sparse sample study were much larger.
so I think I will further pursue exploration of IOV, as Jakob pointed out.
Quoted reply history
________________________________
From: James G Wright <[email protected]>
To: Stephen Duffull <[email protected]>; Mats Karlsson
<[email protected]>; "Ribbing, Jakob" <[email protected]>;
Ethan Wu <[email protected]>; Jurgen Bulitta <[email protected]>;
[email protected]
Cc: Roger Jelliffe <[email protected]>; "Neely, Michael" <[email protected]>
Sent: Thursday, June 18, 2009 6:56:57 AM
Subject: RE: [NMusers] estimating Ka from dataset combining rich sample study
and sparse sampling study
Dear all,
I agree that this kind of behaviour suggests there is some problem with the
model (most likely a lack of exchangeability). I think the ideas suggested are
all good, but the first thing I would try is to separate residual noise for the
two studies with an indicator variable. It is likely that study procedures,
precision of recorded sampling times etc. vary between the two studiess.
Best regards, James
James G WrightPhD
Scientist
Wright Dose Ltd
Tel: 44 (0) 772 5636914
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Stephen Duffull
Sent: 18 June 2009 07:04
To: Mats Karlsson; 'Ribbing, Jakob'; 'Ethan Wu'; 'Jurgen Bulitta';
[email protected]
Cc: 'Roger Jelliffe'; 'Neely, Michael'
Subject: RE: [NMusers] estimating Ka from dataset combining rich sample study
and sparse sampling study
Dear Ethan
I concur with Mats’s comments below.
As a note, from a design perspective adding additional data to an experiment
cannot result in less precise parameter estimates under the assumption that the
individuals from the two data sets are exchangeable. Under this assumption
therefore the Sparse data should merely add information to the Rich data. That
the Sparse data is affecting the parameter estimates from the Rich data
suggests that the two data sets are not exchangeable (different centre,
different assay, different covariates ...).
Another possible way to investigate the differences between the two data sets
would be to analyse them sequentially, perhaps with consideration for using the
analysis from the Rich data as an informative prior for the analysis of the
Sparse data and see where this leads you.
Kind regards
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
From:[email protected] [mailto:[email protected]] On
Behalf Of Mats Karlsson
Sent: Thursday, 18 June 2009 9:17 a.m.
To: 'Ribbing, Jakob'; 'Ethan Wu'; 'Jurgen Bulitta'; [email protected]
Cc: 'Roger Jelliffe'; 'Neely, Michael'
Subject: RE: [NMusers] estimating Ka from dataset combining rich sample study
and sparse sampling study
Dear Ethan,
Variances estimated to be zero may result from fixing off-diagonal variances to
zero (i.e. not using BLOCKs in IIV). Here, however, it may be that there are
systematic differences between the sparse and the rich data experiments. Maybe
fasting/fed status or something else is different. If the fit to the rich data
is markedly worse when including the rich data, at least one parameter is
different between the two situations. I would explore what parameter(s) that
would be. In addition to Jakob’s suggestions below, the two data sets together
may indicate a more complex structural model that a single profile indicated.
Maybe you need to go to a two-compartment for example.
Best regards,
Mats
Mats Karlsson, PhD
Professor of Pharmacometrics
Dept of Pharmaceutical Biosciences
Uppsala University
Box 591
751 24 Uppsala Sweden
phone: +46 18 4714105
fax: +46 18 471 4003
From:[email protected] [mailto:[email protected]] On
Behalf Of Ribbing, Jakob
Sent: Wednesday, June 17, 2009 10:43 PM
To: Ethan Wu; Jurgen Bulitta; [email protected]
Cc: Roger Jelliffe; Neely, Michael
Subject: RE: [NMusers] estimating Ka from dataset combining rich sample study
and sparse sampling study
Hi Ethan,
If OMEGA(?) for KA is drastically reduced when including the sparse data, then
something is wrong with your model and in this case it is not the estimation
method or assumption on distribution of individual parameter). Eta-shrinkage
would not drastically reduce the estimate of OMEGA, since this estimate is
driven by the subjects/studies which contain information on the parameter.
If the sparse data is multiple dosing it may be that KA is variable between
occasions, rather than between subjects (assuming the sparse data contain some
information on KA). Or if the sparse data is from a less well-controlled study
or a different population, it may be that increased IIV in other parts of the
model (e.g. OMEGA on V) is making IIV in KA appear low for the rich study, when
fitting the two studies together. If you get the covariate model in place this
problem will be solved. For the simple model you have it should be quick to
start out assuming that most parameters (THETAs and OMEGAs) are different
between the two studies and then reduce down to a model which is stable and
parsimonious. Obviously, if you eventually can explain the differences using
more mechanistic covariates than study number that is of more use.
Cheers
Jakob