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RE: estimating Ka from dataset combining rich sample study and sparse sampling study

From: James G Wright <james>
Date: Thu, 18 Jun 2009 11:56:57 +0100

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 Wright PhD
Scientist
Wright Dose Ltd
Tel: 44 (0) 772 5636914
 
-----Original Message-----
From: owner-nmusers
On Behalf Of Stephen Duffull
Sent: 18 June 2009 07:04
To: Mats Karlsson; 'Ribbing, Jakob'; 'Ethan Wu'; 'Jurgen Bulitta';
nmusers
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: stephen.duffull
P: +64 3 479 5044
F: +64 3 479 7034
 
Design software: www.winpopt.com
 
 
 
 
 
 
From: owner-nmusers
On Behalf Of Mats Karlsson
Sent: Thursday, 18 June 2009 9:17 a.m.
To: 'Ribbing, Jakob'; 'Ethan Wu'; 'Jurgen Bulitta';
nmusers
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: owner-nmusers
On Behalf Of Ribbing, Jakob
Sent: Wednesday, June 17, 2009 10:43 PM
To: Ethan Wu; Jurgen Bulitta; nmusers
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
 
 

Received on Thu Jun 18 2009 - 06:56:57 EDT

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