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

Smith A, Lambert PC, Rutherford MJ

DOI: 10.1186/s12874-022-01654-1
Published: 2022 Jun 23
Container: BMC medical research methodology
Publisher: Not recorded
Open access: yes

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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 = {Smith A and Lambert PC and Rutherford MJ},
  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  - Smith A
AU  - Lambert PC
AU  - Rutherford MJ
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

A, S., PC, L., & MJ, R. (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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pubmed · retrieved 2026-09-25T15:20:07.852Z