Evidence map›Paper›PMID 38384899›Full record

ReviewJournal of clinical and translational science2024

Peer review of clinical and translational research manuscripts: Perspectives from statistical collaborators.

Phillip J Schulte, Judith D Goldberg, Robert A Oster, Walter T Ambrosius, Lauren Balmert Bonner, Howard Cabral, Rickey E Carter, Ye Chen, Manisha Desai, Dongmei Li and 6 more

Abstract readReview
In one paragraph

Review in Journal of clinical and translational science, 2024. 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

16 authors.

Phillip J SchulteDivision of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.ORCID https://orcid.org/0000-0001-6575-4741
Judith D GoldbergDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
Robert A OsterDepartment of Medicine, Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.ORCID https://orcid.org/0000-0002-2247-8596
Walter T AmbrosiusDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Lauren Balmert BonnerDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Howard CabralDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Rickey E CarterDepartment of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA.
Ye ChenBiostatistics, Epidemiology and Research Design (BERD), Tufts Clinical and Translational Science Institute (CTSI), Boston, MA, USA.
Manisha DesaiQuantitative Sciences Unit, Departments of Medicine, Biomedical Data Science, and Epidemiology and Population Health, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-6949-2651
Dongmei LiDepartment of Clinical and Translational Research, Obstetrics and Gynecology and Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA.
Christopher J LindsellDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.ORCID https://orcid.org/0000-0002-3297-2811
Gina-Maria PomannDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Emily SladeDepartment of Biostatistics, University of Kentucky, Lexington, KY, USA.ORCID https://orcid.org/0000-0002-1654-3822
Tor D TostesonDepartment of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.ORCID https://orcid.org/0000-0001-9302-4762
Fang YuDepartment of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.
Heidi SprattDepartment of Biostatistics and Data Science, School of Public and Population Health, University of Texas Medical Branch, Galveston, TX, USA.ORCID https://orcid.org/0000-0002-9420-5028

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Fred W Kolling IV · 1985 to 2026
$91.3M
NCI NIH HHS P30 CA023108
6 · The paper itself

Abstract

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

Indexed as

Biostatisticsclinical and translational sciencepeer reviewreviewer guidancestudy design

Identifiers

PMID38384899
PMCPMC10879991

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.