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Evaluation metrics for biostatistical and epidemiological collaborations
Author(s) -
Rubio Doris McGartland,
del Junco Deborah J.,
Bhore Rafia,
Lindsell Christopher J.,
Oster Robert A.,
Wittkowski Knut M.,
Welty Leah J.,
Li YiJu,
DeMets Dave
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4184
Subject(s) - biostatistics , data science , computer science , translational research , set (abstract data type) , translational science , process (computing) , management science , public health , medicine , engineering , pathology , operating system , programming language
Increasing demands for evidence‐based medicine and for the translation of biomedical research into individual and public health benefit have been accompanied by the proliferation of special units that offer expertise in biostatistics, epidemiology, and research design (BERD) within academic health centers. Objective metrics that can be used to evaluate, track, and improve the performance of these BERD units are critical to their successful establishment and sustainable future. To develop a set of reliable but versatile metrics that can be adapted easily to different environments and evolving needs, we consulted with members of BERD units from the consortium of academic health centers funded by the Clinical and Translational Science Award Program of the National Institutes of Health. Through a systematic process of consensus building and document drafting, we formulated metrics that covered the three identified domains of BERD practices: the development and maintenance of collaborations with clinical and translational science investigators, the application of BERD‐related methods to clinical and translational research, and the discovery of novel BERD‐related methodologies. In this article, we describe the set of metrics and advocate their use for evaluating BERD practices. The routine application, comparison of findings across diverse BERD units, and ongoing refinement of the metrics will identify trends, facilitate meaningful changes, and ultimately enhance the contribution of BERD activities to biomedical research. Copyright © 2011 John Wiley & Sons, Ltd.