ArticlePharmacology research & perspectives2015
Incorporation of concentration data below the limit of quantification in population pharmacokinetic analyses.
Article in Pharmacology research & perspectives, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers.
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Who cites it
117 citing papers in PubMed.
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- Population Pharmacokinetics of Paritaprevir, Ombitasvir, and Ritonavir in Japanese Patients with Hepatitis C Virus Genotype 1b Infection.Clinical pharmacokinetics · 2017Trial
- Population pharmacokinetic and pharmacodynamic analysis of BIIB023, an anti-TNF-like weak inducer of apoptosis (anti-TWEAK) monoclonal antibody.British journal of clinical pharmacology · 2016Trial
- Pharmacokinetic-Pharmacodynamic Modelling of Repeated Ocrelizumab Dosing in Relapsing-Remitting Multiple Sclerosis.Clinical pharmacokinetics · 2026Observational
- Optimization of Cefotaxime Treatment in Intensive Care Patients: Impact of Inter- and Intra-patient Variability on Pharmacokinetics and Target Attainment.Clinical pharmacokinetics · 2026Article
- Comparing the Predictive Performance of Machine Learning Models with a Newly Developed Population Pharmacokinetic Model for Busulfan in Paediatric Patients.Clinical pharmacokinetics · 2026Article
- Article
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57 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
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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