I would do mixture model only if there is a very large -several folds-
difference in PK parameters for two genotypes. If the difference is
comparable with the inter-subject variability within the genotype, I
would introduce category "missing" to remove the effect of those
subjects on covariate effect estimate. So if the genotype is binary
(YES/NO), you introduce the new third level "missing", work with it as
with the 3-level categorical covariate, and report the difference
between NO and YES as the genotype effect on PK. As a check for
consistency, you may want to check whether the estimate of the PK
parameter for "missing" level is somewhere between the estimates for
"NO" and "YES" levels, closer to the value for the level with higher
prevalence in your dataset.
Regards,
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
On 11/19/2014 6:16 AM, Jeroen Elassaiss-Schaap wrote:
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>http://pd-value.com
[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
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