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Setting Limits on Supersymmetry Using Simplified Models
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Impact of censoring data below an arbitrary quantification limit on structural model misspecification.

Wonkyung Byon1, Courtney V Fletcher, Richard C Brundage

  • 1Department of Experimental and Clinical Pharmacology, University of Minnesota, 308 Harvard St. SE, Minneapolis, MN 55455, USA.

Journal of Pharmacokinetics and Pharmacodynamics
|October 27, 2007
PubMed
Summary

Censoring low pharmacokinetic concentrations below the limit of quantification can lead to incorrect drug model decisions. Adjusting laboratory procedures and using the YLO option can improve accuracy in pharmacokinetic studies.

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Area of Science:

  • Pharmacokinetics
  • Bioanalytical Chemistry
  • Pharmacometrics

Background:

  • Pharmacokinetic (PK) studies often encounter concentrations below the lower limit of quantification (LLOQ), reported as below quantification limit (BQL).
  • While BQL censoring impacts parameter estimates, its effect on PK structural model decisions remains unstudied.
  • Accurate PK modeling is crucial for drug development and clinical decision-making.

Purpose of the Study:

  • To investigate the impact of BQL censored data percentage on PK structural model selection.
  • To evaluate how different coefficients of variation (CV) at LLOQ affect PK model decisions.
  • To test the efficacy of the maximum conditional likelihood estimation (YLO) method in NONMEM VI.

Main Methods:

  • Simulated PK data from a one-compartment intravenous model with 10-50% BQL censoring.
  • Varied LLOQ to achieve CVs of 10%, 20%, 50%, and 100%.
  • Estimated parameters using one- and two-compartment models in NONMEM, employing likelihood ratio tests to assess model fit and the YLO option.

Main Results:

  • Type I error rates significantly increased with higher percentages of BQL censored data, reaching up to 96%.
  • A 10% CV at LLOQ resulted in higher error rates than a 20% CV; error rates normalized with a 100% CV.
  • The YLO option effectively prevented elevated type I error rates.

Conclusions:

  • Assigning LLOQ values can lead to erroneous PK structural model decisions.
  • Analytical laboratory standard operating procedures should provide quantitative values for all samples in drug development.
  • The YLO option is recommended when over 10% of data are BQL censored, while clinical settings may require different precision standards.