Risk Register

Identify, track, and mitigate project risks (e.g., data privacy leaks, biased outcomes, delays).

Format

  • Date Identified: YYYY-MM-DD
  • Risk Description: [What could go wrong]
  • Likelihood: [Low/Medium/High]
  • Impact: [Low/Medium/High]
  • Mitigation Strategy: [How this will be prevented or addressed]

  • Date Identified: 2023-09-20

  • Risk Description: Readers may mistake the synthetic example results for real clinical findings and attempt to implement corresponding operational changes.

  • Likelihood: Low

  • Impact: High

  • Mitigation Strategy: Include bold, highly visible warnings across the index.qmd, README.md, results.qmd, and the Model Card explicitly stating the data is synthetic.

  • Date Identified: 2023-09-22

  • Risk Description: The synthetic data generation script may accidentally replicate an identifiable statistical fingerprint of the researcher’s home institution if based too closely on memory rather than abstract distributions.

  • Likelihood: Low

  • Impact: Medium

  • Mitigation Strategy: Abstract all variables to generic integers/factors and use standard, uncalibrated uniform/poisson distributions without attempting to match local population baselines exactly.