Evidence map›Paper›PMID 38033706›Full record

ArticleJournal of clinical and translational science2023

Essential team science skills for biostatisticians on collaborative research teams.

Emily Slade, Ann M Brearley, Adrian Coles, Matthew J Hayat, Pandurang M Kulkarni, Amy S Nowacki, Robert A Oster, Michael A Posner, Gregory Samsa, Heidi Spratt and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical and translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
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

6 citing papers in PubMed, 11 citations in OpenAlex.

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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

12 authors at 10 institutions in 3 countries.

Emily SladeDepartment of Biostatistics, University of Kentucky, Lexington, KY, USA.ORCID https://orcid.org/0000-0002-1654-3822
Ann M BrearleyDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Adrian ColesGlobal Biometrics and Data Sciences, Bristol Myers Squibb, Lawrence Township, NJ, USA.
Matthew J HayatDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.ORCID https://orcid.org/0000-0002-3252-2255
Pandurang M KulkarniGlobal Data Sciences & Scientific Communications, Eli Lilly & Co., Indianapolis, IN, USA.
Amy S NowackiDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Robert A OsterDivision of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.ORCID https://orcid.org/0000-0002-2247-8596
Michael A PosnerDepartment of Mathematics and Statistics, Villanova University, Villanova, PA, USA.
Gregory SamsaDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, 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
Jesse TroyDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Gina-Maria PomannDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Duke University · USBristol-Myers Squibb (Germany) · DECleveland Clinic Lerner College of Medicine · USEli Lilly (United States) · USGeorgia State University · USThe University of Texas Medical Branch at Galveston · USUniversity of Alabama at Birmingham · USUniversity of Kentucky · USUniversity of Minnesota · USVillanova University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Despite the critical role that quantitative scientists play in biomedical research, graduate programs in quantitative fields often focus on technical and methodological skills, not on collaborative and leadership skills. In this study, we evaluate the importance of team science skills among collaborative biostatisticians for the purpose of identifying training opportunities to build a skilled workforce of quantitative team scientists. Methods: Our workgroup described 16 essential skills for collaborative biostatisticians. Collaborative biostatisticians were surveyed to assess the relative importance of these skills in their current work. The importance of each skill is summarized overall and compared across career stages, highest degrees earned, and job sectors. Results: Survey respondents were 343 collaborative biostatisticians spanning career stages (early: 24.2%, mid: 33.8%, late: 42.0%) and job sectors (academia: 69.4%, industry: 22.2%, government: 4.4%, self-employed: 4.1%). All 16 skills were rated as at least somewhat important by > 89.0% of respondents. Significant heterogeneity in importance by career stage and by highest degree earned was identified for several skills. Two skills ("regulatory requirements" and "databases, data sources, and data collection tools") were more likely to be rated as absolutely essential by those working in industry (36.5%, 65.8%, respectively) than by those in academia (19.6%, 51.3%, respectively). Three additional skills were identified as important by survey respondents, for a total of 19 collaborative skills. Conclusions: We identified 19 team science skills that are important to the work of collaborative biostatisticians, laying the groundwork for enhancing graduate programs and establishing effective on-the-job training initiatives to meet workforce needs.

Indexed as

biostatisticsclinical and translational researchcollaborationdata scienceTeam sciencetraining

Identifiers

PMID38033706
PMCPMC10685263
OpenAlexW4388414774

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.