Abstract
Purpose To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI. Methods A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed. Results For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3. Conclusions Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.
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http://www.cognigencorp.com/nonmem/nm/99aug072006.html; http://www.cognigencorp.com/nonmem/nm/99jul142005.html; http://www.cognigencorp.com/nonmem/nm/99oct041999.html; http://www.cognigencorp.com/nonmem/nm/99jul301999.html; http://www.cognigencorp.com/nonmem/nm/99may201997.html; http://www.boomer.org/pkin/PK02/PK2002364.html. Assessed 26 May 2008
Wakefield J, Racine-Poon A (1995) An application of Bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation. Stat Med 14: 971–986 doi:10.1002/sim.4780140917
Bennett JE, Racine-Poon A, Wakefield JC (1996) MCMC for nonlinear hierarchical models. In: Gilks WR, Richardson S, Spiegelhalter DJ (eds) Markov chain Monte Carlo in practice. Chapman & Hall/CRC
Beal SL (2001) Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn 28: 481–504 doi:10.1023/A:1012299115260
Beal SL, Sheiner LB, Boeckmann AJ (eds) (1989–2006) NONMEM users guides. ICON Development Solutions, Ellicott City, MD
Duval V, Karlsson MO (2002) Impact of omission or replacement of data below the limit of quantification on parameter estimates in a two-compartment model. Pharm Res 19: 1835–1840 doi:10.1023/A:1021441407898
Hing JP, Woolfrey SG, Greenslade D, Wright PMC (2001) Analysis of toxicokinetic data using NONMEM: impact of quantification limit and replacement strategies for censored data. J Pharmacokinet Pharmacodyn 28: 465–479 doi:10.1023/A:1012247131190
Bergstrand M, Plan E, Kjellson MC, Karlsson MO (2007) A comparison of methods for handling of data below the limit of quantification in NONMEM VI. PAGE 16 ISSN 1871-6032
Wang Y (2007) Derivation of various NONMEM estimation methods. J Pharmacokinet Pharmacodyn 34: 575–593 10.1007/s10928-007-9060-6
Abramowiz M, Stegun IA (1964) Handbook of mathematical functions. National Bureau of Standards
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An erratum to this article can be found at http://dx.doi.org/10.1007/s10928-010-9158-0
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Ahn, J.E., Karlsson, M.O., Dunne, A. et al. Likelihood based approaches to handling data below the quantification limit using NONMEM VI. J Pharmacokinet Pharmacodyn 35, 401–421 (2008). https://doi.org/10.1007/s10928-008-9094-4
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DOI: https://doi.org/10.1007/s10928-008-9094-4