Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury

We provide a comprehensive benchmark of data-driven prediction architectures for neurological recovery after SCI. Apart from offering individual patients a specific recovery prediction, these models constitute the basis for synthetic controls. Using real-world data from a completed trial in SCI, we show that synthetic controls could mitigate the challenges of small cohorts and patient recruitment in rare disorders, offering the opportunity to maximize the number of patients receiving an investigative treatment. GITHUB REPOSITORY: https://gitlab.ethz.ch/BMDSlab/publications/sci/sci-in-silico-trials .

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Publication Details

Journal
BMC Medicine
Published
2025-10-24
DOI
https://doi.org/10.1186/s12916-025-04405-3
Citations
1
Primary Topic
Spinal Cord Injury Research
Type
article
Field-Weighted Citation Impact
1.47

Funders

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article

Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury

Norbert Weidner, Rainer Abel, Fred H. Geisler, Frank Röhrich et al.
1 citations
BMC Medicine
Spinal Cord Injury Research
1.47
article

Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury

Norbert Weidner, Rainer Abel, Fred H. Geisler, Frank Röhrich, Thomas Liebscher, Abel Torres‐Espín, Martin Schubert, John L. K. Kramer, Sarah C. Brüningk, Catherine R. Jutzeler, Marc Bolliger, Doris Maier, Armin Curt, Louis P. Lukas, Yorck-Bernhard Kalke, Samuel Håkansson, Miklovana Tuci, Harvinder Singh Chhabra, Rüdiger Rupp, Olga Taran
article en
1 citations

Abstract

We provide a comprehensive benchmark of data-driven prediction architectures for neurological recovery after SCI. Apart from offering individual patients a specific recovery prediction, these models constitute the basis for synthetic controls. Using real-world data from a completed trial in SCI, we show that synthetic controls could mitigate the challenges of small cohorts and patient recruitment in rare disorders, offering the opportunity to maximize the number of patients receiving an investigative treatment. GITHUB REPOSITORY: https://gitlab.ethz.ch/BMDSlab/publications/sci/sci-in-silico-trials .

BMC MedicineVol. 23(1)
University of Bern (CH), SIB Swiss Institute of Bioinformatics (CH), University of British Columbia (CA), University of Waterloo (CA), University of Alberta (CA), University of California, San Francisco (US), Universität Ulm (DE), University of Zurich (CH), Heidelberg University (DE), University Hospital of Bern (CH), University Hospital Heidelberg (DE), Bayreuth Medical Center (DE), University of Saskatchewan (CA), ETH Zurich (CH), Berufsgenossenschaftliche Unfallklinik Murnau (DE), Heidelberg Engineering (Germany) (DE), International Collaboration On Repair Discoveries (CA), BG Klinikum Bergmannstrost Halle (DE), Diagnostic and Treatment Centre Berlin (DE), Neurological Surgery (US), Heidelberg University (US)
National Science Foundation, Wings for Life, International Foundation for Research in Paraplegia, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 14%
Spinal Cord Injury Research
1.47
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