Re: Reporting and handling values below the limit of quantification
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.
Quoted reply history
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]
> ------------------------------------------------
>
> ________________________________
> UNIVERSITY OF CAPE TOWN
>
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--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
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
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http://link.springer.com/article/10.1007/s10928-013-9316-2
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