Evidence map›Paper›PMID 41409507›Full record

ArticleJournal of statistical theory and practice2026

Enhancing Team Science by Training Collaborative Biostatisticians to have a Strong Statistical Voice.

Gina-Maria Pomann, Steven C Grambow, Marissa C Ashner, Bibhas Chakraborty, Nan Liu, Megan L Neely, Sarah Peskoe, Lacey Rende, Emily Slade, Tracy Truong and 3 more

Abstract read
In one paragraph

Article in Journal of statistical theory and practice, 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

13 authors.

Gina-Maria PomannDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0002-6324-3953
Steven C GrambowDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0001-6037-3253
Marissa C AshnerDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0002-2936-4161
Bibhas ChakrabortyCentre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-7366-0478
Nan LiuCentre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-3610-4883
Megan L NeelyDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0002-0101-1081
Sarah PeskoeDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0002-1190-3606
Lacey RendeDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0009-0005-8695-1839
Emily SladeDepartment of Biostatistics, University of Kentucky, Lexington, KY USA.ORCID 0000-0002-1654-3822
Tracy TruongDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0001-5923-4037
Lexie Zidanyue YangDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0002-4843-4313
Greg P SamsaDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0003-2172-1213
Jesse D TroyDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA.ORCID 0000-0001-5410-8146

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
Kentucky Center for Clinical and Translational ScienceUL1TR001998 · NCATS · UNIVERSITY OF KENTUCKY · PI HARTMANN, KATHERINE E, KERN, PHILIP A · 2016 to 2025
$34.2M
Quantitative Team Science (QuanTS) ProgramR25GM155474 · NIGMS · UNIVERSITY OF KENTUCKY · PI Gina-Maria Pomann, Emily Slade · 2024 to 2026
$1.2M
NCATS NIH HHS UL1 TR001998NCATS NIH HHS UL1 TR002553NIGMS NIH HHS R25 GM155474
6 · The paper itself

Abstract

Strong statistical voice is defined as the ability to advocate and negotiate for good and ethical statistical practices, including integrating and resolving differing scientific approaches. This skill is crucial for biostatisticians who work on biomedical research teams, as it ensures the integrity and accuracy of statistical analyses and fosters productive collaborations with non-statisticians. Despite its importance, new graduates often lack targeted training opportunities. This manuscript presents a scalable training approach through the development of online videos. Preliminary didactic materials focused on two key applications: providing written comments on manuscripts and engaging in study design discussions. To evaluate this training approach, a survey was conducted among biostatistics staff in the Duke Biostatistics, Epidemiology, and Research Design Core. The survey results indicated that all respondents strongly agreed on the importance of strong statistical voice in biostatistics practice. The clarity of the training materials and examples received positive feedback, though suggestions for improvement included enhancing video engagement and providing more hands-on training. This information will guide the development of formal training videos embedded within a mentored training program that aims to teach biostatisticians and other quantitative scientists how to effectively work on teams in biomedical research.

Indexed as

Professional developmentStatistical competenciesStrong statistical voiceTeam science trainingWorkforce development

Identifiers

PMID41409507
PMCPMC12705724

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.