Faculty Bibliography

While junior clinical researchers at academic medical institutions across the US often desire to be actively engaged in randomized-clinical trials, they often lack adequate resources and research capacity to design and implement them. This insufficiency hinders their ability to generate a rigorous randomization scheme to minimize selection bias and yield comparable groups. Moreover, there are limited online user-friendly randomization tools. Thus, we developed a free robust randomization app (RRApp). RRApp incorporates 6 major randomization techniques: simple randomization, stratified randomization, block randomization, permuted block randomization, stratified block randomization, and stratified permuted block randomization. The design phase has been completed, including robust server scripts and a straightforward user-interface using the "shiny" package in R. Randomization schemes generated in RRApp can be input directly into the Research Electronic Data Capture (REDCap) system. RRApp has been evaluated by biostatisticians and junior clinical faculty at the Icahn School of Medicine at Mount Sinai. Constructive feedback regarding the quality and functionality of RRApp was also provided by attendees of the 2016 Association for Clinical and Translational Statisticians Annual Meeting. RRApp aims to educate early stage clinical trialists about the importance of randomization, while simultaneously assisting them, in a user-friendly fashion, to generate reproducible randomization schemes.

As clinical researchers at academic medical institutions across the United States increasingly manage complex clinical databases and registries, they often lack the statistical expertise to utilize the data for research purposes. This statistical inadequacy prevents junior investigators from disseminating clinical findings in peer-reviewed journals and from obtaining research funding, thereby hindering their potential for promotion. Underrepresented minorities, in particular, confront unique challenges as clinical investigators stemming from a lack of methodologically rigorous research training in their graduate medical education. This creates a ripple effect for them with respect to acquiring full-time appointments, obtaining federal research grants, and promotion to leadership positions in academic medicine. To fill this major gap in the statistical training of junior faculty and fellows, the authors developed the Applied Statistical Independence in Biological Systems (ASIBS) Short Course. The overall goal of ASIBS is to provide formal applied statistical training, via a hybrid distance and in-person learning format, to junior faculty and fellows actively involved in research at US academic medical institutions, with a special emphasis on underrepresented minorities. The authors present an overview of the design and implementation of ASIBS, along with a short-term evaluation of its impact for the first cohort of ASIBS participants.