ArticleClinical pharmacokinetics2022
"De-Shrinking" EBEs: The Solution for Bayesian Therapeutic Drug Monitoring.
Article in Clinical pharmacokinetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Model-Informed Precision Dosing of Protein Kinase Inhibitors: Evaluation of Model-Averaging and Flattened Priors Methods.Clinical pharmacokinetics · 2026Trial
- Uncertainty undermines the validity of antimicrobial pharmacodynamics.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Multivariate Exact Discrepancy: A New Tool for PK/PD Model Evaluation.Clinical pharmacokinetics · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundTherapeutic drug monitoring (TDM) aims at individualising a dosage regimen and is increasingly being performed by estimating individual pharmacokinetic parameters via empirical Bayes estimates (EBEs). However, EBEs suffer from shrinkage that makes them biased. This bias is a weakness for TDM and probably a barrier to the acceptance of drug dosage adjustments by prescribers.
objectiveThe aim of this article is to propose a methodology that allows a correction of EBE shrinkage and an improvement in their precision.
methodsAs EBEs are defined, they can be seen as a special case of ridge estimators depending on a parameter usually denoted λ. After a bias correction depending on λ, we chose λ so that the individual pharmacokinetic estimations have minimal imprecision. Our estimate is by construction always better than EBE with respect to bias (i.e. shrinkage) and precision.
resultsWe illustrate the performance of this approach with two different drugs: iohexol and isavuconazole. Depending on the patient's actual pharmacokinetic parameter values, the improvement given by our approach ranged from 0 to 100%.
conclusionThis innovative methodology is promising since, to the best of our knowledge, no other individual shrinkage correction has been proposed.
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