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.

A comparative cross-sectional assessment of statistical knowledge of faculty across five health science disciplines

“…We expect little has changed across disciplines since 2017 when these data were collected. However, several recently published papers geared specifically toward medical research learners have the potential to affect positive change [51][52][53]. In the first of these papers, the authors developed a comprehensive set of statistical competencies for medical research learners [51].…”

Translating the learning sciences into practice: A primer for clinical and translational educators

“…Although surprisingly little has been written about learners in the CTSIs and in other organizations that train the translational workforce, we know that learners include graduate students, residents, fellows, faculty, research staff, and community collaborators [25]. They bring with them considerable prior education (bachelor's degrees at minimum and often medical, master's, and doctoral degrees) as well as deep expertise in their fields [26,27]. They tend to have concrete goals for their learning and seek the development of specific, practical skills.…”

How to Write Statistical Analysis Section in Medical Research

“…In this era of evidence-based medicine and practice, basic biostatistical knowledge becomes essential for critically appraising research articles and implementing findings for better patient management, improving healthcare, and research planning. 1 However, it may not be sufficient for the proper execution and reporting of statistical analyses in studies. 2 3 Three things are required for statistical analyses, namely knowledge of the conceptual framework of variables, research design, and evidence-based applications of statistical analysis with statistical software.…”

“…Biostatistics is the overall approach to how we realistically and feasibly execute a research idea to produce meaningful data and translate data to meaningful interpretation and decisions. In this era of evidence-based medicine and practice, basic biostatistical knowledge becomes essential for critically appraising research articles and implementing findings for better patient management, improving healthcare, and research planning 1. However, it may not be sufficient for the proper execution and reporting of statistical analyses in studies 2 3.…”