Evidence map›Paper›PMID 40459798›Full record

ArticleJournal of mammary gland biology and neoplasia2025

biogrowleR: Enhancing Longitudinal Data Analysis.

Carlos Ronchi, Giovanna Ambrosini, Flavia Hughes, Renée L Flaherty, Hazel M Quinn, Daria Matvienko, Andrea Agnoletto, Cathrin Brisken

Abstract read
In one paragraph

Article in Journal of mammary gland biology and neoplasia, 2025. 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

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

1 citing paper in PubMed.

  1. 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

8 authors.

Carlos Ronchi *School of Life Sciences, ISREC - Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland.
Giovanna Ambrosini *School of Life Sciences, ISREC - Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland.
Flavia HughesThe Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, UK.
Renée L FlahertyThe Breast Cancer Now Toby Robins Breast Cancer Research Centre, The Institute of Cancer Research, London, UK.
Hazel M QuinnSchool of Life Sciences, ISREC - Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland.
Daria MatvienkoSchool of Life Sciences, ISREC - Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland.
Andrea AgnolettoSchool of Life Sciences, ISREC - Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland.
Cathrin BriskenSchool of Life Sciences, ISREC - Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland. cathrin.brisken@epfl.ch.

Funding

Breast Cancer Now
6 · The paper itself

Abstract

Time course measurements are used for many applications in biomedical research, ranging from growth curves to drug efficacy testing and high-throughput screening. Statistical methods used to analyze the resulting longitudinal data, such as t-tests or repeated measures ANOVA have limitations when groups are unbalanced, or individual measurements are missing.To address these issues we developed biogrowleR ( https://upbri.gitlab.io/biogrowleR/ ), a workflow to visualize and analyze data based on Frequentist and Bayesian inference combined with hierarchical modeling. By focusing on effect sizes we enhance data interpretation. The workflow further includes a randomization algorithm important to reduce numbers of experimental animals (RRR) and costs. The workflow and R package were designed to be used by researchers with limited experience in R and biostatistics.Our open-source R package biogrowleR contains tutorials, pipelines, and helper functions for the analysis of longitudinal data and enables non computational scientists to perform more effective data analysis.

Indexed as

Data AnalysisSoftwareAlgorithmsAnimalsBayes TheoremData AnalyticsData Interpretation, StatisticalHumansLongitudinal Studies

Identifiers

PMID40459798
PMCPMC12133946

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.