Hi Paul,
As I understand it, you don't have data from all trimesters in all subjects
(and anyhow categorising your data like this may not be helpful), so I don't
think it is appropriate to constrain occasions to correspond to trimesters. I
would include an OCC column which increases for every sampling occasion within
an individual, and then have a BOV term for each of these. This will have the
effect of allowing a single subject's parameters to change with occasion, and
then (provided shrinkage is not an issue) you can plot individual parameters vs
trimester, or better still some continuous scale e.g. week of pregnancy.
Before doing that however, I would be tempted to parameterise your model into
CL V (Q VP... if multi comp) and scale everything for size (linear wt on
volume, and wt^0.75 on CL and Q). Don't fall into the trap of believing women
are not small pregnant women, as perhaps in the case of ranitidine
pharmacokinetics they are (you will know this if your plots of OCC vs pregnancy
week are trendless). If not, you have delineated size from pregnancy effect,
and can test e.g. pregnancy week as a covariate.
BW,
Joe
________________________________________
From: owner-nmus...@globomaxnm.com [owner-nmus...@globomaxnm.com] On Behalf Of
yu...@pitt.edu [yu...@pitt.edu]
Sent: 01 March 2011 15:52
To: nm nm
Subject: [NMusers] [Fwd: occasions during pregnancy]
---------------------------- Original Message ----------------------------
Subject: occasions during pregnancy
From: yu...@pitt.edu
Date: Tue, March 1, 2011 10:49 am
To: "nm nm" <nmus...@blobomaxnm.com>
--------------------------------------------------------------------------
Hi all nmusers,
I thank all who responded my questions yesterday. Almost all the responses
suggested that several occasions of one patient should be put under one ID
#. I re-code my control stream and adjusted the data file as following:
$PK
K12 = THETA(1)*EXP(ETA(1))
CL= THETA(2)*EXP(ETA(2)+ETA(6)*TRI1+ETA(7)*TRI2+ETA(8)*TRI3+ETA(9)*TRI4)
$OMEGA
.8;
.1 .8;
.1 .1 .8;
.1 .1 .1 .8;
.1 .1 .1 .1 .8;
$OMEGA BLOCK(1) 0.9;
$OMEGA BLOCK(1) SAME;
$OMEGA BLOCK(1) SAME;
$OMEGA BLOCK(1) SAME;
where TRI1,TRI2,TRI3, and TRI4 are different stages of pregnancy.
This model fits poorly for the data (from the plot of PRED, IPRED VS. DV),
although the estimates are stable and reasonable.
If I treat the different occasions as different patients, ignoring the
correlation within the same patients, then the model fits quite well and
the results are reasonable.
I also noticed one note from Lewis Sheiner:
Note that, as happens more often, at least with human data, than one might
have thought, the IOV>IIV, then treating each occaasion as though it were
a distinct individual is a reasonable approximation.
--------------Date: Wed, 17 Nov 1999 13:57:18 -0800
From: Lewis Sheiner <le...@c255.ucsf.edu>
Subject: Re: repeating cases---------
The parameters during pregnancy change quite large, so I am not sure if it
is a reasonalble approximation to treat occasions as distinct individual,
or I have to search the better models of putting those occasions under one
ID? and what is the direction to improve the model?
Any suggestion is greatly appreciated.
Paul
School of Pharmacy
University of Pittsburgh
716 Salk Hall
3501 Terrace Street
Pittsburgh, PA 15261
Phone: 412-648-8546
E-mail: yu...@pitt.edu
Yuanyue (Paul) Gao
School of Pharmacy
University of Pittsburgh
716 Salk Hall
3501 Terrace Street
Pittsburgh, PA 15261
Phone: 412-648-8546
E-mail: yu...@pitt.edu
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