Evidence map›Paper›PMID 41979084›Full record

ArticleCPT: pharmacometrics & systems pharmacology2026

Copulas for Covariate Simulation in Pharmacometrics.

Yuchen Guo, Tingjie Guo, J G Coen van Hasselt, Laura B Zwep

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuchen GuoDivision of Systems Pharmacology and Pharmacy, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands.ORCID https://orcid.org/0000-0003-3425-4378
Tingjie GuoDivision of Systems Pharmacology and Pharmacy, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands.ORCID https://orcid.org/0000-0002-7892-6220
J G Coen van HasseltDivision of Systems Pharmacology and Pharmacy, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands.ORCID https://orcid.org/0000-0002-1664-7314
Laura B ZwepDivision of Systems Pharmacology and Pharmacy, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands.ORCID https://orcid.org/0000-0002-6817-2859

Funding

Dutch Research Council (NWO)
6 · The paper itself

Abstract

Patient-specific covariates are commonly incorporated in pharmacometric and quantitative system pharmacology models to predict differences in pharmacokinetic or pharmacodynamic profiles between patients. When simulating new virtual populations of patients, generating realistic covariate sets that accurately reflect the correlation structures among covariates is essential to obtain reliable simulation outcomes. Copulas are joint distribution functions that characterize the dependence structures of patient covariates and enable the simulation of virtual populations. The current tutorial provides a step-by-step guide for understanding the concept of copulas and an overview of applications of copulas in pharmacometric research.

Indexed as

Computer SimulationModels, BiologicalPharmacokineticsHumans

Identifiers

PMID41979084
PMCPMC13274750

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.