Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility

Aiden Smith, Paul C. Lambert, Mark J. Rutherford

Open source

DOI: 10.1186/s12874-022-01654-1
Published: 2022-06-23
Container: BMC Medical Research Methodology
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1186/s12874-022-01654-1,
  title = {Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility},
  author = {Aiden Smith and Paul C. Lambert and Mark J. Rutherford},
  year = {2022},
  journal = {BMC Medical Research Methodology},
  doi = {10.1186/s12874-022-01654-1},
  url = {https://doi.org/10.1186/s12874-022-01654-1}
}

RIS

TY  - JOUR
TI  - Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility
AU  - Aiden Smith
AU  - Paul C. Lambert
AU  - Mark J. Rutherford
PY  - 2022
JO  - BMC Medical Research Methodology
DO  - 10.1186/s12874-022-01654-1
UR  - https://doi.org/10.1186/s12874-022-01654-1
ER  -

APA

Smith, A., Lambert, P. C., & Rutherford, M. J. (2022). Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility. BMC Medical Research Methodology. https://doi.org/10.1186/s12874-022-01654-1

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crossref · retrieved 2026-11-05T23:02:41.978Z