Closing the Gap between Methodologists and End-Users: R as a Computational Back-End

By Byron C. Wallace, Issa J. Dahabreh, Thomas A. Trikalinos, Joseph Lau, Paul Trow, Christopher H. Schmid. 2012

1,132 citations recorded by the source corpus.

The R environment provides a natural platform for developing new statistical methods due to the mathematical expressiveness of the language, the large number of existing libraries, and the active developer community. One drawback to R, however, is the learning curve; programming is a deterrent to non-technical users, who typically prefer graphical user interfaces (GUIs) to command line environments. Thus, while statisticians develop new methods in R, practitioners are often behind in terms of the statistical techniques they use as they rely on GUI applications. Meta-analysis is an instructive example; cutting-edge meta-analysis methods are often ignored by the overwhelming majority of practitioners, in part because they have no easy way of applying them. This paper proposes a strategy to close the gap between the statistical state-of-the-science and what is applied in practice. We present open-source meta-analysis software that uses R as the underlying statistical engine, and Python for the GUI. We present a framework that allows methodologists to implement new methods in R that are then automatically integrated into the GUI for use by end-users, so long as the programmer conforms to our interface. Such an approach allows an intuitive interface for non-technical users while leveraging the latest advanced statistical methods implemented by methodologists.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/09a67730377586a009d440daa6a9ba8f622700b5

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Citations in the corpus

OpenMEE: Intuitive, open‐source software for meta‐analysis in ecology and evolutionary biology (2017, methodology): “Other disciplines in the medical and social sciences have their own dedicated GUI-based software, such as Comprehensive Meta-Analysis (Borenstein et al. 2005), MetaAnalyst (Wallace et al. 2009), OpenMeta-analyst (Wallace et al. 2012), and Rev-Man (Bero & Rennie 1995; Nordic Cochrane Centre 2014).”
Infectious Diseases Society of America Guidelines on the Treatment and Management of Patients with COVID-19 (2022, methodology): “Where applicable, data were pooled using random effects model (fixed effects model for 2 or less trials or pooling of rates) using RevMan or OpenMeta [7].”
Comparative Short-term Benefits of Laparoscopic Liver Resection: 9000 Cases and Climbing (2016, methodology): “OpenMEE software based on OpenMetaAnalyst Software was used for statistical analyses.(20,21) To perform meta-analyses, mean and standard deviation (SD) were needed, and estimations of mean and SD were performed to avoid discarding important studies.”
Peri-implant health and disease. A systematic review of current epidemiology. (2015, methodology): “Meta-analyses were performed to estimate overall prevalence of peri-implant mucositis and periimplantitis (OpenMeta[Analyst]) (Wallace et al. 2012).”
Anxiety and depression in adults with autism spectrum disorder: a systematic review and meta-analysis (2018, methodology): “OpenMeta, a tool for running metafor package in R (Viechtbauer, 2010), was used to conduct the meta-analysis (Wallace et al., 2012).”
Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis (2020, methodology): “The meta-analyses were performed using Stata, and the software OpenMeta[Analyst] [37] and Comprehensive Meta Analysis ve.3.3® licensed for Universidad Tecnológica de Pereira, Colombia.”