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Decision Curve Analysis

Definition
AI-generated

Decision curve analysis evaluates a prediction model (e.g. one including a polygenic risk score) by plotting the net benefit of using the model to treat or screen individuals across a range of risk thresholds, compared with treating all or none.

Topics
Regression and multivariate statistics
Related
Area Under the ROC Curve (AUC) Net Reclassification Improvement (NRI) Polygenic Risk Score (PRS)

Why it matters in GWAS

Unlike AUC alone, decision curves incorporate a clinical threshold model (acceptable risk–benefit trade-offs) and help assess whether adding genetics changes real-world decision-making, especially across ancestries with different calibration.

Example usage

"Decision curve analysis showed limited net benefit of adding the PRS at the guideline threshold in the African-ancestry validation set."

Area Under the ROC Curve (AUC) Net Reclassification Improvement (NRI) Polygenic Risk Score (PRS) Risk Stratification
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References

  • Vickers AJ, Elkin EB. (2006). Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak.
  • Kachuri L, Chatterjee N, Hirbo J, et al. (2024). Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet. https://doi.org/10.1038/s41576-023-00637-2
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