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. 2022 Feb 9;6(1):e418.
doi: 10.21106/ijtmrph.418. Epub 2022 May 27.

Building Physician-Scientist Skills in R Programming:A Short Workshop Report

Muktar H Aliyu  1 , Mahmoud U Sani  2 , Donna J Ingles  3 , Fatima I Tsiga-Ahmed  4 , Baba M Musa  5 , M Shannon Byers  1 , Deepa Dongarwar  6 , Hamisu M Salihu  6 , C William Wester  1
Affiliations

Building Physician-Scientist Skills in R Programming:A Short Workshop Report

Muktar H Aliyu et al. Int J Transl Med Res Public Health. .

Abstract

Introduction: Statistical analysis programs require coding experience and a basic understanding of programming, skills which are not taught as part of medical school or residency curricula.

Methods: We conducted a five-day course for early-career Nigerian physician-scientists interested in learning common statistical tests and acquiring R programming skills. The workshop included didactic presentations, small group learning activities, and interactive discussions. A baseline questionnaire captured participant demographics and solicited participants' level of confidence in understanding/performing common statistical tests. REDCap questionnaires were emailed to obtain feedback on educational format and content. A post-workshop assessment covered participants' overall impression of the program.

Results: A total of 23 participants attended the program. Most participants were male (n=14, 60.9%) and at an early stage in their career (assistant professor, n=20, 87.0%). Approximately 70% of respondents indicated having received some prior training in statistics. The proportion of participants without experience using R and SAS software (90% and 85%, respectively) was greater than the corresponding proportions for Stata (55%) and SPSS (20%). Prior to the workshop, most respondents expressed being "not at all confident" in performing one-way ANOVA (60%), logistic regression (68%), simple linear regression (60%), and McNemar's test (80%). There was a statistically significant post-workshop improvement in the level of confidence in understanding and performing common statistical tests. The course was rated on a 0-100 scale as "moderately difficult" (mean ± SD: 51.7 ± 19.5). Most participants felt comfortable in putting the knowledge learned into practice (82.2 ± 17.1).

Conclusion and public health implications: Introductory R can be taught to junior physician-scientists in resource-limited settings and can inform the development and implementation of similar training initiatives in analogous settings.

Keywords: Low- and Middle-Income Countries; Physician-Scientists; R Programming; Statistical Analysis Training.

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Conflict of interest statement

Conflicts of Interest; No conflict of interest to declare.

Figures

Figure 1.
Figure 1.
Level of Confidence in Performing Specific Statistical Analyses at Baseline
See this image and copyright information in PMC

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