{
  "$type": "site.standard.document",
  "bskyPostRef": {
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    "uri": "at://did:plc:wwyqal4cnqhuwyacdj7rqq3n/app.bsky.feed.post/3miktohfp6xf2"
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  "path": "/t/sample-size-in-prognostic-factor-research/28591#post_4",
  "publishedAt": "2026-04-02T23:09:58.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "koray_durak:\n\n> Schmoor, C., Sauerbrei, W. and Schumacher, M.\n\nFor my exploratory prognostic factor analysis, overfitting considerations for continuous biomarkers were guided by Parmar and Machin (1995) in their book _Survival Analysis: A Practical Approach_ , in which they suggest that the number of candidate predictors in an adjusted survival model should be limited such that it does not exceed the fourth root of the number of observed events, or alternatively that there should be approximately 15–20 events per variable included in the model. However, these rules of thumb are presented as practical guidelines rather than being derived from a formal statistical derivation or simulation-based justification. In addition, the sample size formula developed by Schmoor et al. was derived under the assumption of proportional hazards, which may not hold for biomarkers in real-world settings.",
  "title": "Sample size in prognostic factor research"
}