RE: Negative concentration from simulation
Hi Nyein,
I agree with Nick that it may be valid to simulate negative concentrations and
that the only reason that we don't observe negative concentrations is because
assay labs censor these values. However, for these negative concentrations to
be reasonable and attributed to assay variation, your estimate of the additive
residual error standard deviation should probably be in line with what you
would attribute to assay variation. I have seen model fits using the additive
and proportional residual error model where the additive residual error
variance (or standard deviation) was too large to be attributed to assay
variation. For example, if the additive residual error standard deviation is
larger than the LLOQ that may be too high to be attributed to assay variation.
One thing you could do is a VPC from your model with your observed dataset and
see if you simulate a greater proportion of BQL observations (including
negative concentrations as well as positive concentrations below the LLOQ) than
in your observed dataset. This will help clue you in as to whether your
residual error model is reasonable in simulating very low (and possibly
negative) concentrations.
Best,
Ken
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Nick Holford
Sent: Tuesday, June 2, 2020 3:46 PM
To: [email protected]
Subject: RE: [NMusers] Negative concentration from simulation
Hi Nyein,
For drug concentrations the additive error model assumes that the background
noise is random with mean zero when the drug concentration is truly zero. In
the real world there is always background noise for measurements which means
that real measurements can appear to be a negative concentration even though
the true concentration is zero. Simulations that simulate negative
concentrations are therefore more realistic than those that ignore reality and
are reported as censored measurement values.
The honest thing to do is to report measurements as they are. The dishonest
thing is to report real measurements as below some arbitrary limit of
quantification. There are numerous papers which describe the bias arising from
dishonest reporting of real measurements and work arounds if you have to deal
with this kind of scientific fraud e.g.
Beal SL. Ways to fit a PK model with some data below the quantification limit.
Journal of Pharmacokinetics & Pharmacodynamics. 2001;28(5):481-504.
Duval V, Karlsson MO. Impact of omission or replacement of data below the limit
of quantification on parameter estimates in a two-compartment model. Pharm Res.
2002;19(12):1835-40.
Ahn JE, Karlsson MO, Dunne A, Ludden TM. Likelihood based approaches to
handling data below the quantification limit using NONMEM VI. J Pharmacokinet
Pharmacodyn. 2008;35(4):401-21.
Byon W, Fletcher CV, Brundage RC. Impact of censoring data below an arbitrary
quantification limit on structural model misspecification. J Pharmacokinet
Pharmacodyn. 2008;35(1):101-16.
Senn S, Holford N, Hockey H. The ghosts of departed quantities: approaches to
dealing with observations below the limit of quantitation. Stat Med.
2012;31(30):4280-95.
Keizer RJ, Jansen RS, Rosing H, Thijssen B, Beijnen JH, Schellens JHM, et al.
Incorporation of concentration data below the limit of quantification in
population pharmacokinetic analyses. Pharmacology research & perspectives.
2015;3(2):10.1002/prp2.131
Best wishes,
Nick
--
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 FR+33(6)62 32 46 72
email: [email protected]
http://holford.fmhs.auckland.ac.nz/
http://orcid.org/0000-0002-4031-2514
Read the question, answer the question, attempt all questions
-----Original Message-----
From: [email protected] <[email protected]> On Behalf Of
Bill Denney
Sent: Tuesday, 2 June 2020 8:30 PM
To: Nyein Hsu Maung <[email protected]>; [email protected]
Subject: RE: [NMusers] Negative concentration from simulation
Hi Nyein,
Negative concentrations can be expected from simulations if the model includes
additive residual error. I assume that you mean additive and proportional
error when you say "combined error model". If the error structure does not
include additive error, then we'd need to know more.
How you will handle them in analysis depends on the goals of the analysis.
Usually, you will either simply set negative values to zero or set all values
below the limit of quantification to zero.
Thanks,
Bill
-----Original Message-----
From: [email protected] <[email protected]> On Behalf Of
Nyein Hsu Maung
Sent: Tuesday, June 2, 2020 2:13 PM
To: [email protected]
Subject: [NMusers] Negative concentration from simulation
Dear NONMEM users,
I tried to simulate a new dataset by using a previously published pop pk model.
Their model was described by combined error model for residual variability. And
after simulation, I have obtained two negative concentrations. I would like to
know if there is any proper way to handle those negative concentrations or if
there are some codings to prevent gaining negative concentrations. Thanks.
Best regards,
Nyein Hsu Maung
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