Advertisement

Likelihood based approaches to handling data below the quantification limit using NONMEM VI

  • Jae Eun AhnEmail author
  • Mats O. Karlsson
  • Adrian Dunne
  • Thomas M. Ludden
Article

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.

Keywords

NONMEM VI Limit-of-quantification Likelihood 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    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 CrossRefPubMedGoogle Scholar
  3. 3.
    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/CRCGoogle Scholar
  4. 4.
    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 CrossRefPubMedGoogle Scholar
  5. 5.
    Beal SL, Sheiner LB, Boeckmann AJ (eds) (1989–2006) NONMEM users guides. ICON Development Solutions, Ellicott City, MDGoogle Scholar
  6. 6.
    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 CrossRefPubMedGoogle Scholar
  7. 7.
    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 CrossRefPubMedGoogle Scholar
  8. 8.
    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 1871-6032Google Scholar
  9. 9.
    Wang Y (2007) Derivation of various NONMEM estimation methods. J Pharmacokinet Pharmacodyn 34: 575–593 10.1007/s10928-007-9060-6CrossRefPubMedGoogle Scholar
  10. 10.
    Abramowiz M, Stegun IA (1964) Handbook of mathematical functions. National Bureau of StandardsGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jae Eun Ahn
    • 1
    • 4
    Email author
  • Mats O. Karlsson
    • 2
  • Adrian Dunne
    • 3
  • Thomas M. Ludden
    • 4
  1. 1.Pharmacometrics R & D, ICON Development SolutionsEllicott CityUSA
  2. 2.Department of Pharmaceutical BiosciencesUppsala UniversityUppsalaSweden
  3. 3.School of Mathematical SciencesUniversity of College DublinBelfield, Dublin 4Ireland
  4. 4.Global Pharmacometrics, Pfizer Global Research and DevelopmentNew LondonUSA

Personalised recommendations