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Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data

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  • Ronda Lun
  • Deborah Siegal
  • Tim Ramsay
  • Grant Stotts
  • Dar Dowlatshahi

Abstract

Objectives: Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke. Design: retrospective cohort study. Setting: We used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse). Outcome measures: We compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population. Results: Using synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, p

Suggested Citation

  • Ronda Lun & Deborah Siegal & Tim Ramsay & Grant Stotts & Dar Dowlatshahi, 2024. "Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0295921
    DOI: 10.1371/journal.pone.0295921
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