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Realistic and Robust Reproducible Research for Biostatistics

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Abstract

The complexity of analysis pipelines in biomedical sciences poses a severe challenge for the transparency and reproducibility of results. Researchers are increasingly incorporating software development technologies and methods into their analyses, but this is a quickly evolving landscape and teams may lack the capabilities to set up their own complex IT infrastructure to aid reproducibility. Basing a reproducible research strategy on readily available solutions with zero or low set-up costs whilst maintaining technological flexibility to incorporate domain-specific software tools is therefore of key importance. We outline a practical approach for robust reproducibility of analysis results. In our examples, we rely exclusively on established open-source tools and free services. Special emphasis is put on the integration of these tools with best practices from software development and free online services for the biostatistics domain.
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Dates and versions

hal-03100421 , version 1 (07-01-2021)

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Boris P. Hejblum, Kevin Kunzmann, Ennio Lavagnini, Anna Hutchinson, David Robertson, et al.. Realistic and Robust Reproducible Research for Biostatistics. 2020. ⟨hal-03100421⟩
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