ArticleJournal of pharmacokinetics and pharmacodynamics2016
Accelerating Monte Carlo power studies through parametric power estimation.
Article in Journal of pharmacokinetics and pharmacodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Pharmacokinetic/Pharmacodynamic Modelling of Allopurinol, its Active Metabolite Oxypurinol, and Biomarkers Hypoxanthine, Xanthine and Uric Acid in Hypoxic-Ischemic Encephalopathy Neonates.Clinical pharmacokinetics · 2022Trial
- A pharmacological rationale for improved everolimus dosing in oncology and transplant patients.British journal of clinical pharmacology · 2018Trial
- Informing Sampling Design for Lung Distribution Studies Using a Pulmonary Population Minimal PBPK Model.Clinical pharmacokinetics · 2026Article
- Using Stochastic Simulation-Estimation and Automated Model Development to Assess Power and Accuracy for Covariate Identification.CPT: pharmacometrics & systems pharmacology · 2026Article
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- Integrated Modeling of Digital-Motor and Clinician-Reported Outcomes Using Item Response Theory: Towards Powerful Trials for Rare Neurological Diseases.CPT: pharmacometrics & systems pharmacology · 2025Observational
- Quantitative Comparisons of Progressive Supranuclear Palsy Rating Scale Versions Using Item Response Theory.Movement disorders : official journal of the Movement Disorder Society · 2024Article
- Investigating the influence of relevant pharmacogenetic variants on the pharmacokinetics and pharmacodynamics of orally administered docetaxel combined with ritonavir.The pharmacogenomics journal · 2021Article
- Pharmacometrics-Based Considerations for the Design of a Pharmacogenomic Clinical Trial Assessing Irinotecan Safety.Pharmaceutical research · 2021Article
- Population Pharmacokinetics of Isoniazid, Pyrazinamide, and Ethambutol in Pregnant South African Women with Tuberculosis and HIV.Antimicrobial agents and chemotherapy · 2020Article
- Dose Correction for a Michaelis-Menten Approximation of a Target-Mediated Drug Disposition Model with a Multiple Intravenous Dosing Regimens.The AAPS journal · 2020Article
- Modeling and simulation of the modified Rankin Scale and National Institutes of Health Stroke Scale neurological endpoints in intracerebral hemorrhage.Journal of pharmacokinetics and pharmacodynamics · 2019Article
- Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials.Orphanet journal of rare diseases · 2018Review
- An automated sampling importance resampling procedure for estimating parameter uncertainty.Journal of pharmacokinetics and pharmacodynamics · 2017Article
- Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin: Simulation Study of Power and Predictions Using Model-Based Analysis.CPT: pharmacometrics & systems pharmacology · 2017Article
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Authors and funding
3 authors.
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Abstract
Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models.
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