Open AccessAssessing Statistical Competencies in Clinical and Translational Science Education: One Size Does Not Fit All
Author(s) -
Oster Robert A.,
Lindsell Christopher J.,
Welty Leah J.,
Mazumdar Madhu,
Thurston Sally W.,
Rahbar Mohammad H.,
Carter Rickey E.,
Pollock Bradley H.,
Cucchiara Andrew J.,
Kopras Elizabeth J.,
Jovanovic Borko D.,
Enders Felicity T.
Publication year - 2015
Publication title -
clinical and translational science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.303
H-Index - 44
eISSN - 1752-8062
pISSN - 1752-8054
DOI - 10.1111/cts.12204
Subject(s) - coursework , biostatistics , medical education , translational research , principal (computer security) , translational science , psychology , statistical learning , statistical analysis , computer science , mathematics education , medicine , statistics , artificial intelligence , mathematics , epidemiology , pathology , operating system
Statistics is an essential training component for a career in clinical and translational science (CTS). Given the increasing complexity of statistics, learners may have difficulty selecting appropriate courses. Our question was: what depth of statistical knowledge do different CTS learners require? Methods For three types of CTS learners (principal investigator, co‐investigator, informed reader of the literature), each with different backgrounds in research (no previous research experience, reader of the research literature, previous research experience), 18 experts in biostatistics, epidemiology, and research design proposed levels for 21 statistical competencies. Results Statistical competencies were categorized as fundamental, intermediate, or specialized. CTS learners who intend to become independent principal investigators require more specialized training, while those intending to become informed consumers of the medical literature require more fundamental education. For most competencies, less training was proposed for those with more research background. Discussion When selecting statistical coursework, the learner's research background and career goal should guide the decision. Some statistical competencies are considered to be more important than others. Baseline knowledge assessments may help learners identify appropriate coursework. Conclusion Rather than one size fits all, tailoring education to baseline knowledge, learner background, and future goals increases learning potential while minimizing classroom time.