Re: Validation and power of the Mixture Model
Dear Hui,
The below article will be of interest to you:
Mixture models and subpopulation classification: a PK simulation study and
application to metoprolol CYP2D6 phenotype. J Pharmacokinet Pharmacodyn. 2007
Apr;34(2):141-56.
RegardsChakri
Quoted reply history
On Wednesday, 14 October 2015 1:34 PM, "HUI, Ka Ho"
<[email protected]> wrote:
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div.yiv3867282658WordSection1 {}-->Dear all, I am a fresh research student.
Previously, I had the experience in developing a pharmacogenetics-based
population PK model using NONMEM, but this is the first time I work with the
Mixture Model in NONMEM and I have some questions about it. I have two
datasets on hand, where patients’ clearance are believed to be affected by
their genotypes. While I have their genotypic information, I also wish to know
if the Mixture Model in NONMEM can help me accurately categorize the population
even if the genotypic information are “hidden” from NONMEM. The results turned
out to be unsatisfactory somehow. For the subpopulations with a distinctively
different typical values of clearance, the sensitivity and specificity can
approach 100%, but for those with less differences, the average accuracy drops
to 60-70%. Although it is not difficult to understand that the computer will
not be able to categorize these subjects when they have similar parameters
(either mean values too close or variances too large…), I am wondering if there
is any general approach to utilize the best out of the Mixture Model function.
Regarding the power of the Mixture Model, I wonder if there has been any
validation done before for datasets with different characteristics. For
examples, is there any previous study that looked into the accuracy of the
Mixture Model function and can somehow express the typical accuracy in terms of
the difference in, say, the mean plasma levels, between 2 subpopulations.
Last but not least, it would be great if anyone can kindly advise me any good
teaching materials about the Mixture Model in NONMEM. Sincerely, Matthew Hui