ArticlePharmaceutical research2015
Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques.
Article in Pharmaceutical research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 46 citations in OpenAlex.
- Copulas for Covariate Simulation in Pharmacometrics.CPT: pharmacometrics & systems pharmacology · 2026Article
- Improving severity grading of chemotherapy-induced myelosuppression in AML via data-driven and model-based deep learning.NPJ systems biology and applications · 2026Article
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Bridging the Heterogeneity of Myasthenia Gravis Scores as a Foundational Step Towards the Construction of a Digital Twin.Biomedicines · 2025Article
- Multivariate Normal Distribution Method for a Virtual Cerebral Arterial Population.International journal for numerical methods in biomedical engineering · 2025Article
- Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development.medRxiv : the preprint server for health sciences · 2025Article
- Review
- Information-theoretic evaluation of covariate distributions models.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Sampling from covariate distribution may not always be necessary in PK/PD simulations: illustrative examples with antibiotics.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Optimizing β-lactam-containing antibiotic combination therapy for the treatment of Buruli ulcer.British journal of clinical pharmacology · 2025Article
- Description and Modeling of Relevant Demographic and Laboratory Variables in a Large Oncology Cohort to Generate Virtual Populations.Pharmaceutics · 2024Article
- Generation of realistic virtual adult populations using a model-based copula approach.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- External evaluation of neonatal vancomycin population pharmacokinetic models: Moving from first-order equations to Bayesian-guided therapeutic monitoring.Pharmacotherapy · 2024Article
- 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
- Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.Clinical and translational science · 2024Article
- Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.Journal of medical Internet research · 2024Article
- A practical guide for the generation of model-based virtual clinical trials.Frontiers in systems biology · 2023Article
- Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery.Global spine journal · 2022Article
- Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation forIEEE open journal of engineering in medicine and biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeClinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates.
methodsCOPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods.
resultsOur results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols.
conclusionBoth methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.
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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.