Learning gaps among statistical competencies for clinical and translational science learners

Abstract

Introduction: Statistical literacy is essential in clinical and translational science (CTS). Statistical competencies have been published to guide coursework design and selection for graduate students in CTS. Here, we describe common elements of graduate curricula for CTS and identify gaps in the statistical competencies.

Methods: We surveyed statistics educators using e-mail solicitation sent through four professional organizations. Respondents rated the degree to which 24 educational statistical competencies were included in required and elective coursework in doctoral-level and master's-level programs for CTS learners. We report competency results from institutions with Clinical and Translational Science Awards (CTSAs), reflecting institutions that have invested in CTS training.

Results: There were 24 CTSA-funded respondents representing 13 doctoral-level programs and 23 master's-level programs. For doctoral-level programs, competencies covered extensively in required coursework for all doctoral-level programs were basic principles of probability and hypothesis testing, understanding the implications of selecting appropriate statistical methods, and computing appropriate descriptive statistics. The only competency extensively covered in required coursework for all master's-level programs was understanding the implications of selecting appropriate statistical methods. The least covered competencies included understanding the purpose of meta-analysis and the uses of early stopping rules in clinical trials. Competencies considered to be less fundamental and more specialized tended to be covered less frequently in graduate courses.

Conclusion: While graduate courses in CTS tend to cover many statistical fundamentals, learning gaps exist, particularly for more specialized competencies. Educational material to fill these gaps is necessary for learners pursuing these activities.

Keywords: Statistical competency; biostatistics; clinical and translational science; learning gaps; research training.

Fig. 1.

Coverage of each statistical competency in coursework for doctoral and master’s CTSA programs. For each of the 24 statistical competencies, the percentage of CTSA institutions that rated the competency as “extensively covered in required coursework,” “briefly covered in required coursework,” “covered in elective courses only,” or “not covered in any coursework” are displayed on the x-axis and plotted separately for doctoral (top: n = 11 CTSA programs) and master’s programs (bottom: n = 22 CTSA programs). The mean percent fundamental (with corresponding 95% CI) for each competency as reported in Enders et al. [4] is overlaid on each bar. The percentages for each bar may not sum to 100% due to missing data. Please see Table 1 for the corresponding frequencies and relative frequencies, in addition to a complete description of each competency.

Fig. 2.

Relationship between the percent each of the 24 statistical competencies are covered in CTSA programs versus the percent fundamental. The percentage of CTSA programs that cover each competency (1) extensively in required coursework (left two panels), (2) extensively OR briefly in required coursework (middle two panels), or (3) extensively OR briefly in required coursework OR in elective coursework only (right two panels) is plotted versus the extent to which a competency was perceived as fundamental in prior work [4], separately for doctoral (top three panels: n = 11 CTSA programs) and master’s programs (bottom three panels: n = 22 CTSA programs). The 24 statistical competencies are represented by a dot in each graph. Spearman correlation coefficients and 95% confidence intervals are included on each plot.