Learning gaps among statistical competencies for clinical and translational science learners
Year: 2020
Type: article
Study design: Observational Study
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.
DOI: 10.1017/cts.2020.498
Institutions: University of Alabama at Birmingham, Mayo Clinic, Mayo Clinic in Arizona, University of Rochester, Northwestern University, Northwestern Medicine, Medical University of South Carolina, University of California, Davis, Duke University, The University of Texas Medical Branch at Galveston, Vanderbilt University, Vanderbilt University Medical Center
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,750
Awards: G2762714575, Center for Clinical and Translational Science, G4411448324, Northwestern University Clinical and Translational Science Institute (NUCATS), G522538789, G884562342, G9000001345, G9000037373, G9000049577, G9000054231, G9000111834, G9000127828, G9000162618, G9000244821, G9000261281, G9000263428, G9000282092, Mayo Clinic Center for Clinical and Translational Science (CCaTS), UTMB Clinical and Translational Science Award, G95160140
OpenAlex ID: W3036512088