Statistical competencies for medical research learners: What is fundamental?
Abstract
INTRODUCTION: 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. METHODS:
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.' RESULTS: 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%).
CONCLUSION: 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.
Type
Journal articleSubject
Clinical and Translational ScienceEvidence-Based Medicine
Public Health
Statistical competency
team science
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https://hdl.handle.net/10161/15968Published Version (Please cite this version)
10.1017/cts.2016.31Publication Info
Benn, EKT; Carter, RE; Enders, FT; Grambow, Steven C; Kidwell, KM; Larson, J; ...
Welty, LJ (2017). Statistical competencies for medical research learners: What is fundamental?. J Clin Transl Sci, 1(3). pp. 146-152. 10.1017/cts.2016.31. Retrieved from https://hdl.handle.net/10161/15968.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Steven C. Grambow
Assistant Professor of Biostatistics and Bioinformatics
I am a collaborative statistical scientist with experience spanning a broad range
of clinical research areas, including amyotrophic lateral sclerosis (ALS), post-traumatic
stress disorder (PTSD), Prader-Willi syndrome (PWS), prostate cancer, quality of colorectal
cancer care, osteoarthritis, lifestyle modification through weight loss, CVD risk
reduction through hypertension control, smoking cessation, and substance abuse recovery.
I have experience designing and analyzing observational studie

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