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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
  • , Lexie Zidanyue Yang
  • , Greg P. Samsa
  • , Jesse D. Troy

Producción científica: Articlerevisión exhaustiva

Resumen

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.

Idioma originalEnglish
Número de artículo13
Número de páginas28
PublicaciónJournal of Statistical Theory and Practice
Volumen20
N.º1
DOI
EstadoPublished - mar 2026

Nota bibliográfica

Publisher Copyright:
© The Author(s) 2025.

Financiación

This project was supported by the NIH National Center for Advancing Translational Sciences through grant numbers UL1TR002553 (G-M.P, S.C.G., L.Z.Y., L.R.) and UL1TR001998 (E.S.) and the NIH National Institute of General Medical Science through grant number R25GM155474 (G.M.-P., S.P., L.R., E.S., T.T. G.P.S., J.D.T.). This content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This manuscript presents a framework for training biostatisticians to navigate challenging conversations with non-quantitative team members and enhance their effectiveness within biomedical research teams. The framework includes online training videos with examples and activities evaluated on a sample of its target audience: collaborative biostatisticians embedded within multidisciplinary research teams at Duke University. Survey feedback from the Duke Biostatistics, Epidemiology, and Research Design Methods Core (BERD Core) is reviewed to assist in the development of scalable training resources. The feedback will inform current training within the unit and materials developed for the Quantitative Team Science Program funded by the National Institute of General Medical Sciences (R25GM155474). The manuscript first describes the target audience, next decomposes the critical components of strong statistical voice, then outlines methods for designing materials with tangible didactic value and concludes with considerations for future modifications and national program implementation.

FinanciadoresNúmero del financiador
National Institutes of Health (NIH)
National Institute of General Medical Sciences DP2GM119177 Sophie Dumont National Institute of General Medical SciencesR25GM155474
National Center for Advancing Translational Sciences (NCATS)UL1TR001998, UL1TR002553
NIH National Institute of General Medical ScienceR25GM155474
NIH National Center for Advancing Translational Sciences and Center for Clinical and Translational ScienceUL1TR001998, UL1TR002553

    ASJC Scopus subject areas

    • Statistics and Probability

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