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.