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  "path": "/t/risk-factor-evaluation-in-a-small-surgical-sample-n-20-events-6/28714#post_1",
  "publishedAt": "2026-04-19T00:58:00.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "Dear all\n\nI am evaluating a surgical risk factor in a small retrospective study (**N=20, 6 events**). The potential risk factor is a continuous variable measured during the operation. I am fully aware of the limitations of such a small sample size, but I’ve applied several robust methods to address potential bias and instability. I would like to know if these findings hold some clinical/statistical weight, or if they should be dismissed as “nonsense.”\n\n**Methods & Results:**\n\n  * **Univariable Firth’s Penalized Regression:** OR 1.259 (95% CI: 1.028–1.759), p=0.02.\n\n  * **BCa Bootstrap (1,000 resamples):** 95% CI for OR was 1.008–1.810 (does not cross 1.0).\n\n  * **Internal Validation of the model:** Apparent AUC was 0.7738. Using bootstrap-based optimism correction, the mean optimism was only 0.0016 (Bias-corrected AUC: 0.7722).\n\n\n\n\n**The Question:** Does the fact that the signal survived both Firth’s penalization and BCa-bootstrap correction provide some compelling evidence for a pilot study? Or is it still statistically non-sensical to draw any conclusions from such a small dataset?\n\nI’d appreciate your critical and candid views.\n\n* * *",
  "title": "Risk factor evaluation in a small surgical sample (N=20, Events=6)"
}