Evidence map›Paper›PMID 30058222›Full record

ArticleCPT: pharmacometrics & systems pharmacology2018

A Regression Approach to Visual Predictive Checks for Population Pharmacometric Models.

Kris M Jamsen, Kashyap Patel, Keith Nieforth, Carl M J Kirkpatrick

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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  9. RPEM: Randomized Monte Carlo parametric expectation maximization algorithm.CPT: pharmacometrics & systems pharmacology · 2024
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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.

Kris M JamsenCertara, Inc., Princeton, New Jersey, USA.
Kashyap PatelCertara, Inc., Princeton, New Jersey, USA.
Keith NieforthCertara, Inc., Princeton, New Jersey, USA.
Carl M J KirkpatrickCentre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Vic., Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A visual predictive check (VPC) is a common diagnostic procedure for population pharmacometric models. Typically, VPCs are generated by specifying intervals, or "bins", of an independent variable (e.g., time). However, bin specification is not always straightforward and the choice of bins may affect the appearance, and possibly conclusions, of VPCs. The objective of this work was to demonstrate how regression techniques can be used to derive VPCs and prediction-corrected VPCs (pcVPCs) for population pharmacometric models. This alternative approach negates the need for empirical bin selection. The proposed method utilizes local and additive quantile regression. Implementation is straightforward and computationally acceptable. This work provides support for deriving VPCs and pcVPCs via regression techniques.

Indexed as

Models, BiologicalComputer SimulationHumansRegression Analysis

Identifiers

PMID30058222
PMCPMC6202468

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.