Training Biomedical PhD Students in Applied Biostatistics with R: Lessons from a Three-Year Intensive Program

Statistical literacy is essential for biomedical researchers, yet doctoral training in applied biostatistics and reproducible data analysis remains limited despite the growing accessibility of open-source tools such as R. This retrospective observational programme evaluation examined three consecutive editions (2021–2023) of an intensive three-day doctoral course in applied biostatistics with R, delivered at the Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC) and the Universidad de Córdoba (UCO). Each edition enrolled 15 doctoral students with a 5:1 student-to-instructor ratio and used a problem-based curriculum of nine modules supported by a purpose-written open-access textbook. End-of-course examination performance was described using a ten-item multiple-choice examination; because the examination was not psychometrically equated across editions, score distributions are reported descriptively by cohort rather than used to infer cross-edition differences in learning or competence. Satisfaction was evaluated across four ACSA-aligned domains and was descriptively high in all editions; in the combined within-participant analysis, the Teaching Team domain was rated higher than the other domains. Self-reported theoretical and practical knowledge ratings increased descriptively from pre- to post-course within each edition. The 2021 ratings were reconstructed retrospectively from qualitative responses and were therefore not pooled or directly compared with the directly scaled 2022–2023 ratings. Qualitative feedback highlighted the theory–practice balance and low student-to-instructor ratio, with longer duration as the principal suggestion. The findings support the feasibility and acceptability of this intensive R-based training model, but the uncontrolled design, non-equated examination, small samples, and self-reported outcomes preclude claims about learning improvement, causal effectiveness, or durable competence.