Evidence map›Paper›PMID 36968232›Full record

Trial reportCancer research communications2023

On the Choice of Longitudinal Models for the Analysis of Antitumor Efficacy in Mouse Clinical Trials of Patient-derived Xenograft Models.

Hélène Savel, Sandrine Barbier, Cécile Proust-Lima, Virginie Rondeau, Rodolphe Thiébaut, Florence Meyer-Losic, Laura Richert

Full text readRandomized Controlled Trial
In one paragraph

Trial report in Cancer research communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Hélène SavelDepartment of Public Health, Inserm Bordeaux Population Health Research Centre, U1219, University of Bordeaux, Bordeaux, France.ORCID 0000-0003-4685-4039
Sandrine BarbierIpsen Innovation, Les Ulis, France.ORCID 0000-0002-3370-2346
Cécile Proust-LimaDepartment of Public Health, Inserm Bordeaux Population Health Research Centre, U1219, University of Bordeaux, Bordeaux, France.ORCID 0000-0002-9884-955X
Virginie RondeauDepartment of Public Health, Inserm Bordeaux Population Health Research Centre, U1219, University of Bordeaux, Bordeaux, France.ORCID 0000-0001-7109-4831
Rodolphe ThiébautDepartment of Public Health, Inserm Bordeaux Population Health Research Centre, U1219, University of Bordeaux, Bordeaux, France.ORCID 0000-0002-5235-3962
Florence Meyer-LosicIpsen Innovation, Les Ulis, France.ORCID 0000-0003-0245-2855
Laura RichertDepartment of Public Health, Inserm Bordeaux Population Health Research Centre, U1219, University of Bordeaux, Bordeaux, France.ORCID 0000-0002-7682-2225

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In translational oncology research, the patient-derived xenograft (PDX) model and its use in mouse clinical trials (MCT) are increasingly described. This involves transplanting a human tumor into a mouse and studying its evolution during follow-up or until death. A MCT contains several PDXs in which several mice are randomized to different treatment arms. Our aim was to compare longitudinal modeling of tumor growth using mixed and joint models. Mixed and joint models were compared in a real MCT ( Significance: This work brings new arguments to a controversy on the correct choice of statistical modeling methods for the analysis of MCTs. We conclude that mixed models are more robust than joint models.

Indexed as

Models, StatisticalNeoplasmsAnimalsComputer SimulationDisease Models, AnimalHeterograftsHumansMice

Identifiers

PMID36968232
PMCPMC10035449

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read59
Read underepoch 390

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.