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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.
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.
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.
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.
Acute care research (ACR) is uniquely challenged by the constraints of recruiting participants and conducting research procedures within minutes to hours of an unscheduled critical illness or injury. Existing competencies for clinical research professionals (CRPs) are gaining traction but may have gaps for the acute environment. We sought to expand existing CRP competencies to include the specialized skills needed for ACR settings.
Qualitative data collected from job shadowing, clinical observations, and interviews were analyzed to assess the educational needs of the acute care clinical research workforce. We identified competencies necessary to succeed as an ACR-CRP, and then applied Bloom’s Taxonomy to develop characteristics into learning outcomes that frame both knowledge to be acquired and job performance metrics.
There were 28 special interest competencies for ACR-CRPs identified within the eight domains set by the Joint Task Force (JTF) of Clinical Trial Competency. While the eight domains were not prioritized by the JTF, in ACR an emphasis on Communication and Teamwork, Clinical Trials Operations, and Data Management and Informatics was observed. Within each domain, distinct proficiencies and unique personal characteristics essential for success were identified. The competencies suggest that a combination of competency-based training, behavioral-based hiring practices, and continuing professional development will be essential to ACR success.
The competencies developed for ACR can serve as a training guide for CRPs to be prepared for the challenges of conducting research within this vulnerable population. Hiring, training, and supporting the development of this workforce are foundational to clinical research in this challenging setting.
It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from ‘fundamental’ (necessary for all) to ‘specialized’ (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized.
We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by ‘fundamental’ and ‘specialized.’
There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one’s own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%).
We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.
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