---------------------------- Original Message ----------------------------
Subject: occasions during pregnancy
From: [email protected]
Date: Tue, March 1, 2011 10:49 am
To: "nm nm" <[email protected]>
--------------------------------------------------------------------------
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 <[email protected]>
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: [email protected]
Yuanyue (Paul) Gao
School of Pharmacy
University of Pittsburgh
716 Salk Hall
3501 Terrace Street
Pittsburgh, PA 15261
Phone: 412-648-8546
E-mail: [email protected]
[Fwd: occasions during pregnancy]
Hi Paul,
When you treat each occasion as a different patient you get
subject-by-occasion specific predictions of all the parameters in your model
(or at least for those parameters in which you include an IIV eta)...not
just clearance. Thus, it is not surprising that you might get a better fit
by doing this relative to your code below. If IOV>IIV on your other
parameters you might consider evaluating IOV on other parameters in addition
to CL. If your model is for PO dosing, you might also consider putting IOV
on a relative F parameter as often differences in apparent CL/F and apparent
V/F between occasions is often due to differences in absorption between
occasion rather than due to differences in the disposition kinetics for CL
and V.
Ken
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of [email protected]
Sent: Tuesday, March 01, 2011 10:53 AM
To: nm nm
Subject: [NMusers] [Fwd: occasions during pregnancy]
---------------------------- Original Message ----------------------------
Subject: occasions during pregnancy
From: [email protected]
Date: Tue, March 1, 2011 10:49 am
To: "nm nm" <[email protected]>
--------------------------------------------------------------------------
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 <[email protected]>
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: [email protected]
Yuanyue (Paul) Gao
School of Pharmacy
University of Pittsburgh
716 Salk Hall
3501 Terrace Street
Pittsburgh, PA 15261
Phone: 412-648-8546
E-mail: [email protected]
Paul,
You might try plotting your etas 6-9 vs. trimester (coded at four levels) to
ensure that the IOV is truly random by occasion, as your model assumes.
Obviously, it is not unheard of that the IOV should be much larger than IIV,
but I wouldn't start with that assumption. Usually there is at least some
correlation within an individual. Good luck.
Kevin
Kevin Dykstra, PhD, FCP
+1 978.655.1943 (O)
+1 978.289.2987 (M)
[email protected] | http://qPharmetra.com
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of [email protected]
Sent: Tuesday, March 01, 2011 10:53 AM
To: nm nm
Subject: [NMusers] [Fwd: occasions during pregnancy]
---------------------------- Original Message ----------------------------
Subject: occasions during pregnancy
From: [email protected]
Date: Tue, March 1, 2011 10:49 am
To: "nm nm" <[email protected]>
--------------------------------------------------------------------------
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 <[email protected]>
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: [email protected]
Yuanyue (Paul) Gao
School of Pharmacy
University of Pittsburgh
716 Salk Hall
3501 Terrace Street
Pittsburgh, PA 15261
Phone: 412-648-8546
E-mail: [email protected]
Hi,
Within subject variability has two parts -- a random component that is you have tried to describe using between occasion variability (AKA IOV) -- and a fixed (or predictable) component that in your case is associated with each stage of pregnancy.
Your code only has the random component for within subject variability in it. I suggest you include a stage of pregnancy covariate effect on your PK parameters. That would then resolve the discrepancy in goodness of fit you found between treating each stage as a different subject compared with only using random BOV to explain differences.
Whether the random differences in parameter variability are larger within subjects ("IOV") compared to within subjects ("IIV")has no intrinsic importance and you do not have to make any assumption about their relative magnitude. It is just a description of the way things are. For example within subject variability in oral bioavailability will often be bigger than between subject variability because it is determined by day to day vagaries of absorption. On the other hand between subject variability in volume of distribution will usually be larger than within subject variability because there are usually rather small random changes from day to day.
Note that in your case the fixed effect (predictable) changes in volume in different stages of pregnancy may be quite large and account for a large fraction of your current estimates using a random effect ("IOV") alone. The fixed effect changes in pregnancy are likely to be the most interesting part of your analysis to your clinical colleagues so don't forget them :-)
Best wishes,
Nick
Quoted reply history
On 2/03/2011 5:59 a.m., Kevin Dykstra wrote:
> Paul,
> You might try plotting your etas 6-9 vs. trimester (coded at four levels) to
> ensure that the IOV is truly random by occasion, as your model assumes.
> Obviously, it is not unheard of that the IOV should be much larger than IIV,
> but I wouldn't start with that assumption. Usually there is at least some
> correlation within an individual. Good luck.
> Kevin
>
> Kevin Dykstra, PhD, FCP
>
> +1 978.655.1943 (O)
> +1 978.289.2987 (M)
> [email protected] | http://qPharmetra.com
>
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On
> Behalf Of [email protected]
> Sent: Tuesday, March 01, 2011 10:53 AM
> To: nm nm
> Subject: [NMusers] [Fwd: occasions during pregnancy]
>
> ---------------------------- Original Message ----------------------------
> Subject: occasions during pregnancy
> From: [email protected]
> Date: Tue, March 1, 2011 10:49 am
> To: "nm nm"<[email protected]>
> --------------------------------------------------------------------------
>
> 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<[email protected]>
> 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: [email protected]
>
> Yuanyue (Paul) Gao
>
> School of Pharmacy
> University of Pittsburgh
> 716 Salk Hall
> 3501 Terrace Street
> Pittsburgh, PA 15261
> Phone: 412-648-8546
> E-mail: [email protected]
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology& Clinical Pharmacology
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
Paul
Another suggestion
As a general rule, drug clearance increases during pregnancy due to increased
blood perfusion, especially during the last trimesters. You might also consider
a time covariate effect over your clearance rather than on etas, with fewer
parameters.
Best wishes,
Armel
Armel Stockis
UCB Pharma
Brussels
Quoted reply history
----- Original Message -----
From: Kevin Dykstra [mailto:[email protected]]
Sent: Tuesday, March 01, 2011 05:59 PM
To: [email protected] <[email protected]>; 'nm nm' <[email protected]>
Subject: RE: [NMusers] [Fwd: occasions during pregnancy]
Paul,
You might try plotting your etas 6-9 vs. trimester (coded at four levels) to
ensure that the IOV is truly random by occasion, as your model assumes.
Obviously, it is not unheard of that the IOV should be much larger than IIV,
but I wouldn't start with that assumption. Usually there is at least some
correlation within an individual. Good luck.
Kevin
Kevin Dykstra, PhD, FCP
+1 978.655.1943 (O)
+1 978.289.2987 (M)
[email protected] | http://qPharmetra.com
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of [email protected]
Sent: Tuesday, March 01, 2011 10:53 AM
To: nm nm
Subject: [NMusers] [Fwd: occasions during pregnancy]
---------------------------- Original Message ----------------------------
Subject: occasions during pregnancy
From: [email protected]
Date: Tue, March 1, 2011 10:49 am
To: "nm nm" <[email protected]>
--------------------------------------------------------------------------
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 <[email protected]>
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: [email protected]
Yuanyue (Paul) Gao
School of Pharmacy
University of Pittsburgh
716 Salk Hall
3501 Terrace Street
Pittsburgh, PA 15261
Phone: 412-648-8546
E-mail: [email protected]
--------------------------------------------------------
UCB Pharma S.A.
Allée de la Recherche, 60 1070 Brussels, Belgium
Tel: +32.2.559.99.99 - Fax: +32.2.559.92.10
Registration number : RPM/RPR Brussels 0403.096.168
VAT BE 0403.096.168 - Bank 210-0045962-36
--------------------------------------------------------
Legal Notice: This electronic mail and its attachments are intended solely for
the person(s) to whom they are addressed and contain information which is
confidential or otherwise protected from disclosure, except for the purpose for
which they are intended. Dissemination, distribution, or reproduction by anyone
other than the intended recipients is prohibited and may be illegal. If you are
not an intended recipient, please immediately inform the sender and return the
electronic mail and its attachments and destroy any copies which may be in your
possession. UCB screens electronic mails for viruses but does not warrant that
this electronic mail is free of any viruses. UCB accepts no liability for any
damage caused by any virus transmitted by this electronic mail. (Ref: #*UBP1208)
Paul
As another "rule" protein binding decreases throughout pregnancy to a low at
term due to dilution. This with a purported increase in CL makes for
interesting modelling.
I think as an initial approach a biologically plausible model is better than
moving down the various variance routes. Once you have something that you
think describes the biology then I think it would be important and interesting
to explore inclusion of hierarchical random effects.
Regards
Steve
--
Professor Stephen Duffull
Chair of Clinical Pharmacy
School of Pharmacy
University of Otago
PO Box 56 Dunedin
New Zealand
E: [email protected]
P: +64 3 479 5044
F: +64 3 479 7034
W: http://pharmacy.otago.ac.nz/profiles/stephenduffull
Design software: www.winpopt.com
Quoted reply history
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On
> Behalf Of [email protected]
> Sent: Wednesday, 2 March 2011 8:24 a.m.
> To: [email protected]; [email protected]; [email protected]
> Subject: Re: [NMusers] [Fwd: occasions during pregnancy]
>
> Paul
> Another suggestion
> As a general rule, drug clearance increases during pregnancy due to increased
> blood perfusion, especially during the last trimesters. You might also
> consider a time covariate effect over your clearance rather than on etas, with
> fewer parameters.
>
> Best wishes,
> Armel
>
> Armel Stockis
> UCB Pharma
> Brussels
>
> ----- Original Message -----
> From: Kevin Dykstra [mailto:[email protected]]
> Sent: Tuesday, March 01, 2011 05:59 PM
> To: [email protected] <[email protected]>; 'nm nm' <[email protected]>
> Subject: RE: [NMusers] [Fwd: occasions during pregnancy]
>
> Paul,
> You might try plotting your etas 6-9 vs. trimester (coded at four levels) to
> ensure that the IOV is truly random by occasion, as your model assumes.
> Obviously, it is not unheard of that the IOV should be much larger than IIV,
> but I wouldn't start with that assumption. Usually there is at least some
> correlation within an individual. Good luck.
> Kevin
>
> Kevin Dykstra, PhD, FCP
>
> +1 978.655.1943 (O)
> +1 978.289.2987 (M)
> [email protected] | http://qPharmetra.com
>
>
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On
> Behalf Of [email protected]
> Sent: Tuesday, March 01, 2011 10:53 AM
> To: nm nm
> Subject: [NMusers] [Fwd: occasions during pregnancy]
>
> ---------------------------- Original Message ----------------------------
> Subject: occasions during pregnancy
> From: [email protected]
> Date: Tue, March 1, 2011 10:49 am
> To: "nm nm" <[email protected]>
> --------------------------------------------------------------------------
>
> 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 <[email protected]>
> 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: [email protected]
>
>
> Yuanyue (Paul) Gao
>
> School of Pharmacy
> University of Pittsburgh
> 716 Salk Hall
> 3501 Terrace Street
> Pittsburgh, PA 15261
> Phone: 412-648-8546
> E-mail: [email protected]
>
> --------------------------------------------------------
>
> UCB Pharma S.A.
> Allée de la Recherche, 60 1070 Brussels, Belgium
> Tel: +32.2.559.99.99 - Fax: +32.2.559.92.10
> Registration number : RPM/RPR Brussels 0403.096.168
> VAT BE 0403.096.168 - Bank 210-0045962-36
> --------------------------------------------------------
>
> Legal Notice: This electronic mail and its attachments are intended solely for
> the person(s) to whom they are addressed and contain information which is
> confidential or otherwise protected from disclosure, except for the purpose
> for which they are intended. Dissemination, distribution, or reproduction by
> anyone other than the intended recipients is prohibited and may be illegal. If
> you are not an intended recipient, please immediately inform the sender and
> return the electronic mail and its attachments and destroy any copies which
> may be in your possession. UCB screens electronic mails for viruses but does
> not warrant that this electronic mail is free of any viruses. UCB accepts no
> liability for any damage caused by any virus transmitted by this electronic
> mail. (Ref: #*UBP1208)
>
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
Quoted reply history
________________________________________
From: [email protected] [[email protected]] On Behalf Of
[email protected] [[email protected]]
Sent: 01 March 2011 15:52
To: nm nm
Subject: [NMusers] [Fwd: occasions during pregnancy]
---------------------------- Original Message ----------------------------
Subject: occasions during pregnancy
From: [email protected]
Date: Tue, March 1, 2011 10:49 am
To: "nm nm" <[email protected]>
--------------------------------------------------------------------------
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 <[email protected]>
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: [email protected]
Yuanyue (Paul) Gao
School of Pharmacy
University of Pittsburgh
716 Salk Hall
3501 Terrace Street
Pittsburgh, PA 15261
Phone: 412-648-8546
E-mail: [email protected]
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to do so is strictly prohibited and may be unlawful.
Thank you for your co-operation.
NHSmail is the secure email and directory service available for all NHS staff
in England and Scotland
NHSmail is approved for exchanging patient data and other sensitive information
with NHSmail and GSi recipients
NHSmail provides an email address for your career in the NHS and can be
accessed anywhere
For more information and to find out how you can switch, visit
www.connectingforhealth.nhs.uk/nhsmail
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