ArticleJournal of pharmacokinetics and pharmacodynamics2012
Extension of NPDE for evaluation of nonlinear mixed effect models in presence of data below the quantification limit with applications to HIV dynamic model.
Article in Journal of pharmacokinetics and pharmacodynamics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Population Pharmacokinetics of AL-335 and Its Two Main Metabolites (ALS-022399, ALS-022227) in Monotherapy and in Combination with Odalasvir and/or Simeprevir.The AAPS journal · 2018Trial
- Population pharmacokinetics and dose-response relationships of mitoxantrone in children with acute myeloid leukaemia.British journal of clinical pharmacology · 2026Article
- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?European journal of drug metabolism and pharmacokinetics · 2024Article
- Developing Tools to Evaluate Non-linear Mixed Effect Models: 20 Years on the npde Adventure.The AAPS journal · 2021Review
- Population Pharmacokinetic Model of Oxfendazole and Metabolites in Healthy Adults following Single Ascending Doses.Antimicrobial agents and chemotherapy · 2021Article
- What to Do About Missed Doses? A Retrospective Study of Olanzapine in the Elderly.Drug design, development and therapy · 2021Article
- Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy.Journal of pharmacokinetics and pharmacodynamics · 2020Article
- A Regression Approach to Visual Predictive Checks for Population Pharmacometric Models.CPT: pharmacometrics & systems pharmacology · 2018Article
- Development and performance of npde for the evaluation of time-to-event models.Pharmaceutical research · 2018Article
- Individual Bayesian Information Matrix for Predicting Estimation Error and Shrinkage of Individual Parameters Accounting for Data Below the Limit of Quantification.Pharmaceutical research · 2017Article
- Maturation of Oxycodone Pharmacokinetics in Neonates and Infants: a Population Pharmacokinetic Model of Three Clinical Trials.Pharmaceutical research · 2017Article
- Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics.CPT: pharmacometrics & systems pharmacology · 2017Article
- Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: a Novel Method to Handle the Interval Censoring Caused by Measurement of Smaller Tumors.The AAPS journal · 2016Article
- Platelet Dynamics in Peritoneal Carcinomatosis Patients Treated with Cytoreductive Surgery and Hyperthermic Intraperitoneal Oxaliplatin.The AAPS journal · 2016Article
- Neutrophil dynamics in peritoneal carcinomatosis patients treated with cytoreductive surgery and hyperthermic intraperitoneal oxaliplatin.Clinical pharmacokinetics · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data below the quantification limit (BQL data) are a common challenge in data analyses using nonlinear mixed effect models (NLMEM). In the estimation step, these data can be adequately handled by several reliable methods. However, they are usually omitted or imputed at an arbitrary value in most evaluation graphs and/or methods. This can cause trends to appear in diagnostic graphs, therefore, confuse model selection and evaluation. We extended in this paper two metrics for evaluating NLMEM, prediction discrepancies (pd) and normalised prediction distribution errors (npde), to handle BQL data. For a BQL observation, the pd is randomly sampled in a uniform distribution over the interval from 0 to the probability of being BQL predicted by the model, estimated using Monte Carlo (MC) simulation. To compute npde in presence of BQL observations, we proposed to impute BQL values in both validation dataset and MC samples using their computed pd and the inverse of the distribution function. The imputed dataset and MC samples contain original data and imputed values for BQL data. These data are then decorrelated using the mean and variance-covariance matrix to compute npde. We applied these metrics on a model built to describe viral load obtained from 35 patients in the COPHAR 3-ANRS 134 clinical trial testing a continued antiretroviral therapy. We also conducted a simulation study inspired from the real model. The proposed metrics show better behaviours than naive approaches that discard BQL data in evaluation, especially when large amounts of BQL data are present.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.