Cited by 200Pharmaceutical studies and practicesStatistical Methods in Clinical TrialsStatistical Methods and Bayesian Inference

Incorporation of concentration data below the limit of quantification in population pharmacokinetic analyses

Open access
Keizer, Ron J., Jansen, Robert S., Rosing, Hilde, Thijssen, Bas, Beijnen, Jos H., Schellens, Jan H. M., Huitema, Alwin D. R.·2015·Pharmacology Research & Perspectives

Abstract

Handling of data below the lower limit of quantification (LLOQ), below the limit of quantification (BLOQ) in population pharmacokinetic (PopPK) analyses is important for reducing bias and imprecision in parameter estimation. We aimed to evaluate whether using the concentration data below the LLOQ has superior performance over several established methods. The performance of this approach ("All data") was evaluated and compared to other methods: "Discard," "LLOQ/2," and "LIKE" (likelihood-based). An analytical and residual error model was constructed on the basis of in-house analytical method validations and analyses from literature, with additional included variability to account for model misspecification. Simulation analyses were performed for various levels of BLOQ, several structural PopPK models, and additional influences. Performance was evaluated by relative root mean squared error (RMSE), and run success for the various BLOQ approaches. Performance was also evaluated for a real PopPK data set. For all PopPK models and levels of censoring, RMSE values were lowest using "All data." Performance of the "LIKE" method was better than the "LLOQ/2" or "Discard" method. Differences between all methods were small at the lowest level of BLOQ censoring. "LIKE" method resulted in low successful minimization (<50%) and covariance step success (<30%), although estimates were obtained in most runs (∼90%). For the real PK data set (7.4% BLOQ), similar parameter estimates were obtained using all methods. Incorporation of BLOQ concentrations showed superior performance in terms of bias and precision over established BLOQ methods, and shown to be feasible in a real PopPK analysis.

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Crossref · 10.1002/prp2.131

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  • Concentration data below LOQ but not below LOD were included to evaluate the excretion profile because inclusion can result in a better fit and excluding or replacing them with zero could lead to biased pharmacokinetic parameters.

    “Concentration data below the LOQ (but not below the LOD) were included to evaluate the excretion profile, as their inclusion can result in a better fit of the excretion profile model, while excluding or replacing them with zero could lead to biased pharmacokinetic parameters [39,40,41,42].”

    From Urinary Elimination of Ecdysterone and its Metabolites following a Single-Dose Administration in Humans (2021) · cites this paper as [39]

    The Materials and Methods section says the paper included detectable concentrations below the LLOQ but discarded those below the LOD. The Results and Conclusion report improved bias and precision in population PK analyses compared with discarding such data. The paper does not evaluate an excretion-profile model or replacing concentrations with zero, so those parts of the claim are unsupported. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. Ahn C An evaluation of phase I cancer clinical trial designs Stat Med 1998 17 1537 1549 9699228

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Authors
Keizer, R. J., Jansen, R. S., Rosing, H., Thijssen, B., Beijnen, J. H., Schellens, J. H. M., & Huitema, A. D. R.
Affiliations
  • University of California, San Francisco (US)
  • Slotervaartziekenhuis (NL)
  • Utrecht University (NL)
Corresponding author
Keizer, Ron J.
Venue
Pharmacology Research & Perspectives
Publisher
Wiley
Published
2015-03
Volume
3
Issue
2
Pages
e00131-e00131
ISSN
2052-1707 (ISSN-L)
OpenAlex
W1984026103
Cited by
200
References
32
Citations by year
2026: 21 · 2025: 33 · 2024: 19 · 2023: 14 · 2022: 25 · 2021: 23 · 2020: 16 · 2019: 18 · 2018: 12 · 2017: 9 · 2016: 9 · 2015: 1
FWCI
10.32
Type
journal article
Language
en
Open access
Locations
  • Pharmacology Research & Perspectives (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
population pharmacokinetics, data handling, analytical method validation, residual error model, model misspecification, parameter estimation bias, simulation study

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