ESMARConf2021 Session 4B (Data Viz): Luke McGuinness
Presenter: Luke McGuinness
Session: Data Viz ( • ESMARConf2021 livestream Session 4B -... )
Title: "On the shoulders of giants": advantages and challenges to building on established evidence synthesis packages, using the {robvis} package as a case study
Abstract: {robvis} is an established R package which allows users to create publication-quality risk-of-bias plots. Recently, it has expanded its scope to allow for new functionality, focusing in particular on better integration with the {metafor} package for meta-analysis. Following a public discussion of the proposed functionality run through GitHub and Twitter, in addition to consultation with the maintainer of the {metafor} package, two new functions were added to {robvis}. The first creates paired risk-of-bias plots, where a ""traffic light""-style risk-of-bias plot is appended onto a standard forest plot so that the risk of bias for each result in the meta-analysis is readily available to the reader. This function builds on the output of the {metafor} forest plot function to create these graphs. In addition, as users frequently wish to stratify their analysis by the level of bias in each included study, a new function which automatically subsets the data by this variable and presents a subgroup meta-analysis for each subgroup has also been developed. This presentation will use the {robvis} package as a case study to demonstrate how collaboration between packages in the evidence synthesis landscape can result in new functionality and increased ease-of-use for end users. It will also highlight the benefit of thinking of the evidence synthesis workflow as a whole when developing and expanding new functionality, rather than seeing packages as standalone silos.
Please cite as: McGuinness, Luke (2021): "On the shoulders of giants": advantages and challenges to building on established evidence synthesis packages, using the {robvis} package as a case study. Presented at ESMARConf2021. figshare. Conference contribution. https://doi.org/10.6084/m9.figshare.1...
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