Strategies for Developing Biostatistics Resources in an Academic Health Center
ABSTRACT Biostatistics-the application of statistics to understanding health and biology-provides powerful tools for developing research questions, designing studies, refining measurements, analyzing data, and interpreting findings. Biostatistics plays an important role in health-related research, yet biostatistics resources are often fragmented, ad hoc, or oversubscribed within academic health centers (AHCs). Given the increasing complexity and quantity of health-related data, the emphasis on accelerating clinical and translational science, and the importance of conducting reproducible research, the need for the thoughtful development of biostatistics resources within AHCs is growing.In this article, the authors identify strategies for developing biostatistics resources in three areas: (1) recruiting and retaining biostatisticians, (2) efficiently using biostatistics resources, and (3) improving biostatistical contributions to science. AHCs should consider these three domains in building strong biostatistics resources, which they can leverage to support a broad spectrum of research. For each of the three domains, the authors describe the advantages and disadvantages of AHCs creating centralized biostatistics units rather than dispersing such resources across clinical departments or other research units. They also address the challenges that biostatisticians face in contributing to research without sacrificing their individual professional growth or the trajectory of their research teams. The authors ultimately recommend that AHCs create centralized biostatistics units because this approach offers distinct advantages both to investigators who collaborate with biostatisticians as well as to the biostatisticians themselves, and it is better suited to accomplish the research and education missions of AHCs.
- Pediatric Radiology 08/2014; 44(8):933-4. DOI:10.1007/s00247-013-2823-x · 1.65 Impact Factor
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ABSTRACT: IntroductionStatistics 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.ResultsStatistical 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.DiscussionWhen 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.Clinical and Translational Science 09/2014; 8(1). DOI:10.1111/cts.12204 · 2.11 Impact Factor
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ABSTRACT: The Research Development Core (RDC) is housed within the Michigan Institute for Clinical & Health Research (MICHR) at the University of Michigan (U-M). Established in 2006, RDC provides no-cost, in-person consultations to help U-M investigators strengthen their grant proposals. RDC offers investigators feedback and critique on all aspects of their study design, plus partnerships, funding mechanisms, and future directions. This article describes RDC's model and provides data describing the success of its services.RDC is composed of a multidisciplinary team of professionals in grant development. It comprises two senior faculty codirectors from the U-M Medical School, two senior biostatisticians, outside faculty content experts, and RDC administrative staff. Investigators contact RDC to request a consultation and submit advance grant materials for review by the RDC team. During the consultation, investigators explain their project and identify challenges. The RDC team and additional experts offer feedback that is captured in meeting notes and provided to investigators. RDC commitments beyond the meetings are implemented and carefully tracked. Investigators may also request grant editing, budgeting, or proposal submission assistance. Investigators using RDC have been awarded $44.5 million since 2011.The demand for RDC consultations doubled from 2010 to 2011 and reached a high of 131 consultations in 2012. Investigator feedback has been positive: 80% reported that RDC had a strong impact on their proposal, and over 90% indicated that they would recommend RDC to colleagues. MICHR is committed to providing investigators with RDC services to better ensure strong grant applications and successful research careers.Academic medicine: journal of the Association of American Medical Colleges 10/2014; 90(1). DOI:10.1097/ACM.0000000000000535 · 3.47 Impact Factor