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

From: Ribbing, Jakob <Jakob.Ribbing>
Date: Wed, 17 Jun 2009 22:21:13 +0100

Hi Ethan,

 

IOV on KA is often more pronounced than on CL or V, so I would start
there.

 

To account for a higher IIV in the MD study, just estimate a theta for
the ratio of %CV MD over %CV SD:

 

CL=TVCL*EXP(ETA(X)*(1+THETA(Y)*MD))

 

Where MD is a 0 or 1 indicator of study.

 

Since the two studies are from the same population considering a more
complex structural model, like Mats suggests, would also make a lot of
sense. It all depends on how long you can follow the SD profiles.

 

I hope this helps!

 

Jakob

 

________________________________

From: Ethan Wu [mailto:ethan.wu75
Sent: 17 June 2009 22:05
To: Ribbing, Jakob; Jurgen Bulitta; nmusers
Cc: Roger Jelliffe; Neely, Michael
Subject: Re: [NMusers] estimating Ka from dataset combining rich sample
study and sparse sampling study

 

Hi Jakob,

   sparse data came from MD study. and IIV on CL increased from 0.14 to
0.25, on V from 0.185 to 0.196 after inclusion of sparse data

   both in the same population.

 

  I think what you suggest making sense to me. I would keep Eta on Ka
first, start exploring IOV on CL and V, then explore covariates on CL
and V, to see if decreasing IIV on CL and V would leads to more
reasonable estimate of IIV on Ka.

  

  but, overall, I think that it is the stress of shrinkage on Ka leads
to "dumping" IIV to CL and V, not something wrong with the model itself.

 

  

 

________________________________

From: Ribbing, Jakob <Jakob.Ribbing
To: Ethan Wu <ethan.wu75
<jbulitta
Cc: Roger Jelliffe <jelliffe
Sent: Wednesday, June 17, 2009 4:43:28 PM
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 Wed Jun 17 2009 - 17:21:13 EDT

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