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Related Experiment Videos

Ways to fit a PK model with some data below the quantification limit.

S L Beal1

  • 1Department of Laboratory Medicine, University of California, San Francisco 94143-0626, USA.

Journal of Pharmacokinetics and Pharmacodynamics
|January 5, 2002
PubMed
Summary

Seven methods for handling below quantification limit (BQL) drug concentrations in pharmacokinetic modeling were evaluated. Using BQL data as censored observations offers statistical advantages, though gains may be small with infrequent BQLs.

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Area of Science:

  • Pharmacokinetics and Pharmacometric Modeling
  • Biopharmaceutical Analysis

Background:

  • Pharmacokinetic (PK) data frequently include concentrations below the quantification limit (BQL).
  • Handling BQL observations is crucial for accurate pharmacokinetic model fitting.
  • Current methods for BQL data involve either discarding or specialized statistical treatment.

Purpose of the Study:

  • To evaluate seven distinct methods for managing BQL observations in pharmacokinetic data analysis.
  • To compare the statistical performance of different BQL handling techniques using simulated data.

Main Methods:

  • Simulation of single-subject and population pharmacokinetic data from a one-compartment model.
  • Evaluation of methods including: discarding BQLs, treating them as censored data, imputation with 0, and imputation with half the quantification limit (QL/2).

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  • Assumed coefficient of variation (CV) for measurements at the quantification limit was ≤20%, aligning with FDA guidance.

    • Main Results:

    • Treating BQL observations as fixed-point censored data demonstrated overall statistical advantages.
    • The benefit of censored data methods over simple BQL discarding was modest, particularly when BQL frequency was low.
    • Imputation with QL/2 improved population parameter estimation but degraded population variance estimation.

    Conclusions:

    • Handling BQL data as censored observations is statistically advantageous in pharmacokinetic modeling.
    • The choice of method depends on the frequency of BQL observations and the specific modeling goals (e.g., parameter estimation vs. variance estimation).
    • Simple imputation methods like using 0 should be avoided, while QL/2 imputation has specific trade-offs for population PK.