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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.

Document Type

Article

Publication Date

2026

Notes/Citation Information

© The Author(s) 2025

Digital Object Identifier (DOI)

https://doi.org/10.1007/s42519-025-00522-7

Funding Information

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.

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