Evidence map›Paper›PMID 29575824›Full record

ArticleCPT: pharmacometrics & systems pharmacology2018

Complex Bayesian Modeling Workflows Encoding and Execution Made Easy With a Novel WinBUGS Plugin of the Drug Disease Model Resources Interoperability Framework.

Cristiana Larizza, Elisa Borella, Lorenzo Pasotti, Palma Tartaglione, Mike Smith, Stuart Moodie, Paolo Magni

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. The Standard Output: A Tool-Agnostic Modeling Storage Format.CPT: pharmacometrics & systems pharmacology · 2018
    Article
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

7 authors.

Cristiana LarizzaDepartment of Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy.
Elisa BorellaDepartment of Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy.
Lorenzo PasottiDepartment of Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy.
Palma TartaglioneDepartment of Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy.
Mike SmithDepartment of Statistical Pharmacometrics, Pfizer Global Research and Development, Sandwich, Kent, UK.
Stuart MoodieEight Pillars, Edinburgh, UK.
Paolo MagniDepartment of Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Drug Disease Model Resources (DDMoRe) Interoperability Framework (IOF) enables pharmacometric model encoding and execution via Model Description Language (MDL) and R language, through the ddmore package. Through its components and converter plugins, the IOF can execute pharmacometric tasks using different target tools, starting from a single MDL-encoded model. In this article, we present the WinBUGS plugin and show how its integration in the IOF enables an easy implementation of complex Bayesian workflows. We selected a published diabetes-linked study as a real-world example, in which two inter-related models are used to estimate insulin secretion rate in response to a glucose stimulus from intravenous glucose tolerance test (IVGTT) data. This model was implemented following different approaches to propagate uncertainty, via diverse IOF target tools (NONMEM, WinBUGS, PsN, and Xpose). The developed software supports a plethora of pharmacokinetic/pharmacodynamic (PK/PD) modeling features. It provides solutions to reproducibility and interoperability issues in Bayesian modeling, and facilitates the difficult encoding of complex PK/PD models in WinBUGS.

Indexed as

Models, BiologicalPharmacokineticsBayes TheoremGlucose Tolerance TestHumansModels, StatisticalReproducibility of ResultsSoftware

Identifiers

PMID29575824
PMCPMC6561612

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LicenceCC BY-NC
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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.