Paulo,
I wish you luck in trying to do this. I also spend some time trying to
persuade the people in your lab to do intelligent things with their
measurements when I was in Cape Town.
I suggest you might try looking at this:
http://holford.fmhs.auckland.ac.nz/docs/censored-observations-with-nonmem.pdf
I discuss the FDA Guidance that is usually used by the chemical analysts
to support their deliberate attempts to make our life difficult.
Unfortunately this is largely an issue of belief not science. It is
essentially impossible to win religious battles with wisdom. The usual
strategy to win a religious war is with guns and bombs. I don't
recommend that. But perhaps you might try drugs -- e.g. your excellent
South African wine.
In Auckland I was able to persuade one LC-MS chemical analyst to see the
light and he reported his measurements honestly (including some negative
concentration measurements). A complex PK model was published based on
these truthful observations (Patel et al. 2011).
Once you have honest observations I think it is much easier to decide
how to model the residual error. The additive error component can then
be a realistic description of assay background noise.
Best wishes,
Nick
Patel K, Choy SS, Hicks KO, Melink TJ, Holford NH, Wilson WR. A combined
pharmacokinetic model for the hypoxia-targeted prodrug PR-104A in
humans, dogs, rats and mice predicts species differences in clearance
and toxicity. Cancer Chemother Pharmacol. 2011;67(5):1145-55.
On 7/11/2013 4:33 a.m., Paolo Denti wrote:
Dear all,
I know I am opening a bit of a can of worms here, and one that has been
opened before, but please bear with me..
We are trying to make our case with our analytical laboratory to
convince them to release to us (pharmacometrics) the values below the
limit of quantification (BLQ), which they normally define as the level
below which they can't guarantee 20% CV on the measurement.
So far, they have been quite reluctant, because they say that this would
go against their SOPs, quality assurance policies, some FDA and EMA
guidelines, and what not. However, after months of insisting, it seems
like they may finally be open for discussion and asked us to present as
much supporting evidence and experience from other labs as possible.
Our main argument is that censoring BLQ values may be a reasonable
policy when the data needs to be used for other purposes or by
clinicians, but for us modelers it is a terrible waste of information,
because we have tools to properly deal with the additional level of
uncertainty,
My first question to the group is then the following - Nick, I
explicitly count on you for this one... :)
1. Can you suggest any literature/guidelines/references in support of
our cause?
a. Any literature clearly advocating for/supporting the release of the
BLQ values for pharmacometric modelling.
b. Any official guidelines providing/justifying an exception to the
standard practice of censoring when the data is handled with modelling
c. Any personal experience with your lab or the regulatory authority
about this topic
So far, I've found some previous threads here on NMUsers and the
conclusion section in this paper:
Byon W, Fletcher C V, Brundage RC. Impact of censoring data below an
arbitrary quantification limit on structural model misspecification. J.
Pharmacokinet. Pharmacodyn. 35: 101–16, 2008.
The second question is about how to handle these values if we manage to
get them (fingers crossed).
The released data will have some actual values below the assay
validation limit (that we can call "low precision"), and some that will
be NA, because sometimes the mass-spec will not be able to identify a
peak in the elution profile.
2. What error structure would you recommend to handle a dataset
including uncensored BLQ values?
a. Should one fix the additive component of the error to a fraction of
the LLOQ (say 50%)? And if so, for all samples, even the ones above
LLOQ, or only the BLQ ones?
b. How would you handle the NAs? Would you impute 0? Impute the lowest
value reported? Half of it?
c. If you have a series of NAs to impute, would you retain only the
first one and exclude the following, or would include them all? Would
you have the proportional component of the error apply also to the
imputed NAs or not?
Any input and help is greatly appreciated!
Greetings from Cape Town,
Paolo
--
------------------------------------------------
Paolo Denti, PhD
Pharmacometrics Group
Division of Clinical Pharmacology
Department of Medicine
University of Cape Town
K45 Old Main Building
Groote Schuur Hospital
Observatory, Cape Town
7925 South Africa
phone: +27 21 404 7719
fax: +27 21 448 1989
email: [email protected]
------------------------------------------------
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--
Nick Holford, Professor Clinical Pharmacology
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University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
office:+64(9)923-6730 mobile:NZ +64(21)46 23 53
email: [email protected]
http://holford.fmhs.auckland.ac.nz/
Holford NHG. Disease progression and neuroscience. Journal of Pharmacokinetics
and Pharmacodynamics. 2013;40:369-76
http://link.springer.com/article/10.1007/s10928-013-9316-2
Holford N, Heo Y-A, Anderson B. A pharmacokinetic standard for babies and
adults. J Pharm Sci. 2013:
http://onlinelibrary.wiley.com/doi/10.1002/jps.23574/abstract
Holford N. A time to event tutorial for pharmacometricians. CPT:PSP. 2013;2:
http://www.nature.com/psp/journal/v2/n5/full/psp201318a.html
Holford NHG. Clinical pharmacology = disease progression + drug action. British
Journal of Clinical Pharmacology. 2013:
http://onlinelibrary.wiley.com/doi/10.1111/bcp.12170/abstract
