Alzheimer's Clinical Research Data via R Packages: the alzverse
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916985763266560 |
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| author | Donohue, Michael C. Hussen, Kedir Langford, Oliver Gallardo, Richard Jimenez-Maggiora, Gustavo Aisen, Paul S. |
| author_facet | Donohue, Michael C. Hussen, Kedir Langford, Oliver Gallardo, Richard Jimenez-Maggiora, Gustavo Aisen, Paul S. |
| contents | Sharing clinical research data is essential for advancing research in Alzheimer's disease (AD) and other therapeutic areas. However, challenges in data accessibility, standardization, documentation, usability, and reproducibility continue to impede this goal. In this article, we highlight the advantages of using R packages to overcome these challenges using two examples. The A4LEARN R package includes data from a randomized trial (the Anti-Amyloid Treatment in Asymptomatic Alzheimer's [A4] study) and its companion observational study of biomarker negative individuals (the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration [LEARN] study). The ADNIMERGE2 R package includes data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a longitudinal observational biomarker and imaging study. These packages collect data, documentation, and reproducible analysis vignettes into a portable bundle that can be installed and browsed within commonly used R programming environments. We also introduce the alzverse package which leverages a common data standard to combine study-specific data packages to facilitate meta-analyses. By promoting collaboration, transparency, and reproducibility, R data packages can play a vital role in accelerating clinical research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02318 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Alzheimer's Clinical Research Data via R Packages: the alzverse Donohue, Michael C. Hussen, Kedir Langford, Oliver Gallardo, Richard Jimenez-Maggiora, Gustavo Aisen, Paul S. Computation Applications Sharing clinical research data is essential for advancing research in Alzheimer's disease (AD) and other therapeutic areas. However, challenges in data accessibility, standardization, documentation, usability, and reproducibility continue to impede this goal. In this article, we highlight the advantages of using R packages to overcome these challenges using two examples. The A4LEARN R package includes data from a randomized trial (the Anti-Amyloid Treatment in Asymptomatic Alzheimer's [A4] study) and its companion observational study of biomarker negative individuals (the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration [LEARN] study). The ADNIMERGE2 R package includes data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a longitudinal observational biomarker and imaging study. These packages collect data, documentation, and reproducible analysis vignettes into a portable bundle that can be installed and browsed within commonly used R programming environments. We also introduce the alzverse package which leverages a common data standard to combine study-specific data packages to facilitate meta-analyses. By promoting collaboration, transparency, and reproducibility, R data packages can play a vital role in accelerating clinical research. |
| title | Alzheimer's Clinical Research Data via R Packages: the alzverse |
| topic | Computation Applications |
| url | https://arxiv.org/abs/2510.02318 |