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 -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Nick Holford Sent: Tuesday, June 2, 2020 3:46 PM To: nmusers@globomaxnm.com 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: n.holf...@auckland.ac.nz 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: owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> On Behalf Of Bill Denney Sent: Tuesday, 2 June 2020 8:30 PM To: Nyein Hsu Maung <nyeinhsumaung2...@gmail.com>; nmusers@globomaxnm.com 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: owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> On Behalf Of Nyein Hsu Maung Sent: Tuesday, June 2, 2020 2:13 PM To: nmusers@globomaxnm.com 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 -- This email has been checked for viruses by Avast antivirus software. https://www.avast.com/antivirus
