RE: Genotype data missing in some individuals
Dear SoJeong,
I agree with everything Jeroen proposed. In addition to that, you may want to
code the subjects with missing genotype as genotype 99 (or something similar)
and then estimate genotype as categorical covariate on CL. This approach is not
elegant but it is quick and often useful for initial analysis.
Kind regards
Dinko
"The contents of this message are mine personally and do not necessarily
reflect any position of the Government or the Food and Drug Administration."
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Jeroen Elassaiss-Schaap
Sent: Wednesday, November 19, 2014 6:16 AM
To: 이소정
Cc: [email protected]
Subject: Re: [NMusers] Genotype data missing in some individuals
Dear SoJeong,
First you might want to answer the question whether that phenotype is indeed
important in your dataset. With the initial popPK model you could plot posthoc
clearance against bodyweight and/or inspect the posthocs of clearance for
evidence of multiple peaks in your distribution. You also may see the impact of
phenotype in stratified concentration versus time plots. Depending on the
dataset, with its sampling scheme, number of subjects (perhaps a low number)
and distribution across age, it could be masked.
If the impact is clear however, it might be benificial to try to include the
subjects wih missing genotype. With a clear effect, you might be able to
develop a mixture model. The mixture approach would describe the different
populations in your dataset corresponding to the different phenotypes. The
genotype would than inform the mixture as a covariate - the missing information
would fall back to the pure mixture approach. As a warning, this approach is
quite difficult. I would advise you to read up on the nonmem guides ($MIX) on
this and look in the literature for examples - the Karlsson group has published
about it, most recently this one (it contains code):
http://link.springer.com/article/10.1208/s12248-009-9093-4. A search in the
literature gives you additional background such as
http://www.page-meeting.org/pdf_assets/9595-PAGE2007_3.pdf and
http://link.springer.com/article/10.1007/s10928-006-9038-9.
If the impact is not clear, a more empirical approach might be called for, in
this case a subset analysis, i.e. where you exclude the missing subjects, of
the covariate relationship might be all that you could achieve. If there is no
impact at all, you do not need the genotype of course.
Hope this helps!
Best regards,
Jeroen
http://pd-value.com
[email protected]<mailto:[email protected]>
@PD_value
+31 6 23118438
-- More value out of your data!
On Nov 19, 2014, at 7:57 AM, "이소정"
<[email protected]<mailto:[email protected]>> wrote:
Dear all,
I’ve analyzed a tacrolimus PopPK in pediatric patients.
As you know, CYP3A5 genotype can change the tacrolimus PK significantly, 3A5
genotyping was performed in the study,
however, in 20% of the subjects, the genotype data was missed.
Then, how can I reflect the CYP3A5 genotype effect to the tacrolimus population
model appropriately?
Is there any solution?
Best regards,
SoJeong Yi