NONMEM Users Network Archive

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RE: Meta-analysis with Nonmem

From: Elodie Plan <elodie.plan>
Date: Thu, 4 Mar 2010 14:33:21 +0100

Dear Andreas,

 

An approach you may consider is to model both the standard deviation and =
the
average as dependent variables:

 

SD=THETA(1)*EXP(ETA(1))

AV=(THETA(2)+DOSE*THETA(3))*EXP(ETA(2))

 

SESD=SD/(2*(N-1)**.5)

SEAV=SD/N**.5

 

IF(FLAG.EQ.0) THEN

Y = SD+SESD*EPS(1)

ELSE

Y = AV+SEAV*EPS(1)

ENDIF

 

$SIGMA 1 FIX

 

Your dataset would treat study as individuals, include N the number of
individuals in the study arm and incorporate a flag, so would look like:

STUD=ID DV N FLAG DOSE

1 SD1 N1 0 D1

1 AV1 N1 1 D1

1 SD2 N2 0 D2

1 AV2 N2 1 D2



 

Note: this is only valid when there was only 1 endpoint per subject.

Hope this helps.

 

Best regards,

Elodie, Martin and Mats

 

Elodie L. Plan, PharmD, MSc, PhD student

********************************************

Uppsala Pharmacometrics Research Group,

Department of Pharmaceutical Biosciences,
P.O. Box 591, SE-751 24 Uppsala, SWEDEN

Office +46 18-471 4385, Fax +46 18-471 4003

 

From: owner-nmusers
On
Behalf Of alindauer-research
Sent: Friday, February 26, 2010 10:26 AM
To: nmusers
Subject: Re: [NMusers] Meta-analysis with Nonmem

 


Thank you Leonid and Dirk for your replies. They were very helpful.

Leonid, what would you think about the following:
Y=IPRED+SE*EPS(1)+ETA(1)
With SIGMA fixed to 1 and OMEGA being the inter-study =
standard-deviation?
What would be the interpretation of SIGMA if it were not fixed to 1?
You wrote:
>>Y=IPRED+SE*EXP(ETA(1))*EPS(1)), etc.
Wouldn't this be somehow transformed by NONMEM because of the =
linearization
process? Similarly to why we cannot use the exponential residual error =
model
directly in NONMEM, but have to do the transform-both-sides approach.

Dirk, good idea. However, estimation of the simulated datasets might =
become
very time consuming when you combine data from several large studies. =
And,
as you said:
>> If the number of subjects in a study is small, it might be useful to
repeat the simulation and produce different combined datasets.
I would even go further and say, that it is absolutly necessary to =
repeat
the simulation-reestimation procedure many times in order to get =
"stable"
results.

Best regards, Andreas.


Ferrer


Andreas Lindauer


Pharmacokineticist


Pharmacokinetics and Metabolism


R&D Center. Ferrer Internacional S.A.


Juan de Sada 32, 08028 Barcelona

        
        

alindauer-research


www.ferrergrupo.com

        
        
        
        
        

 


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Received on Thu Mar 04 2010 - 08:33:21 EST

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