scholarly journals Developing an Applied Biostatistical Sciences (ABS) network

Author(s):  
Shokoufeh Khalatbari ◽  
Dianne Jazdzyk ◽  
Janine Capsouras ◽  
Brad Downey ◽  
Eli Samuels ◽  
...  

Abstract Introduction: Access to qualified biostatisticians to provide input on research design and statistical considerations is critical for high-quality clinical and translational research. At diverse health science institutions, like the University of Michigan (U-M), biostatistical collaborators are scattered across the campus. This model can isolate applied statisticians, analysts, and epidemiologists from each other, which may negatively affect their career development and job satisfaction, and inhibits access to optimal biostatistical support for researchers. Furthermore, in the era of modern, complex translational research, it is imperative to elevate biostatistical expertise by offering innovative training. Methods: The Michigan Institute for Clinical and Health Research established an Applied Biostatistical Sciences (ABS) network that is a campus-wide community of staff and faculty statisticians, epidemiologists, data scientists, and researchers, with the intention of supporting both researchers and biostatisticians, while promoting high-quality clinical and translational research. Results: Since its inception in early 2018, the ABS Network has grown to several hundred faculty and staff members across a range of health and research disciplines. The ABS Network offers free trainings on innovative methods and tools in the biostatistical field, a web-based portal with resources and training lectures, and connections to U-M faculty and/or staff members for consultation and collaboration. Conclusions: Although challenging, if approached strategically, the creation of a collaboration network of biostatisticians can be accomplished. Furthermore, the process can be adopted and implemented for establishing collaboration with any network of professionals with common interests across different disciplines and professional fields regardless of size.

2021 ◽  
Vol 78 (15) ◽  
pp. 1564-1568
Author(s):  
Fred M. Kusumoto ◽  
John A. Bittl ◽  
Mark A. Creager ◽  
Harold L. Dauerman ◽  
Anuradha Lala ◽  
...  

2014 ◽  
Vol 7 (5) ◽  
pp. 406-412 ◽  
Author(s):  
Estela S. Estapé‐Garrastazu ◽  
Carlamarie Noboa‐Ramos ◽  
Lizbelle Jesús‐Ojeda ◽  
Zulmarie Pedro‐Serbiá ◽  
Edna Acosta‐Pérez ◽  
...  

2017 ◽  
Vol 78 (3) ◽  
pp. 272 ◽  
Author(s):  
Marci D. Brandenburg ◽  
Sigrid Anderson Cordell ◽  
Justin Joque ◽  
Mark P. MacEachern ◽  
Jean Song

Librarians are excellent research collaborators, although librarian participation is not usually considered, thereby making access to research funds difficult. The University of Michigan Library became involved in the university’s novel funding program, MCubed, which supported innovative interdisciplinary research on campus, primarily by funding student assistants to work on research projects. This article discusses three different MCubed projects that all benefited from librarian involvement. These projects spanned across many areas from translational research to systematic reviews to digital humanities. Librarian roles ranged from mentoring and project management to literature searching.


2018 ◽  
Vol 2 (4) ◽  
pp. 249-252
Author(s):  
Andrew L. Sussman ◽  
Carla Cordova ◽  
Mark R. Burge

Recruitment and engagement for clinical and translational research is challenging, especially among medically underserved and ethnic or racial minority populations. We present a comprehensive model developed through the Clinical and Translational Science Center at the University of New Mexico (UNM) Health Sciences Center that addresses 3 critical aspects of participant recruitment. The components of the model are: (1) Recruitment from within UNM to UNM-centered studies, (2) recruitment from within UNM to community-based studies, and (3) recruitment from outside UNM to UNM-centered studies. This model has increased research participant recruitment, especially among medically underserved populations, and offers generalizable translational solutions to common clinical and translational research challenges, especially in settings with similar demographic and geographic characteristics.


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