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

From: Mats Karlsson <mats.karlsson>
Date: Wed, 17 Jun 2009 23:16:31 +0200

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


Best regards,



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
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


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.







Received on Wed Jun 17 2009 - 17:16:31 EDT

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