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Area of Science:

  • Biostatistics
  • Medical Decision Making
  • Risk Modeling

Background:

  • Decision curve analysis is crucial for evaluating the clinical utility of risk prediction models.
  • Accurate estimation of net benefit (NB) across various risk thresholds is essential for informed decision-making.
  • Existing methods for decision curve estimation have limitations regarding model calibration and data requirements.

Purpose of the Study:

  • To present and compare three distinct methods for estimating decision curves.
  • To introduce robust inference techniques for decision curve analysis.
  • To provide methods for comparing the performance of two risk models at specific risk thresholds.

Main Methods:

  • Estimation of NB using full validation cohort data (risks R, event indicator Y), robust to miscalibration.
  • Precise NB estimation assuming model calibration, with potential bias if miscalibrated.
  • Utilizing case-control data with known event incidence for NB estimation.
  • Employing influence functions for variance estimation and bootstrap for confidence bands.
  • Extending variance estimation to complex survey samples.

Main Results:

  • The method using (R, Y) on the full cohort is robust to model miscalibration.
  • Assuming calibration allows for more precise NB estimates but risks bias if the model is miscalibrated.
  • Case-control data offers comparable efficiency to full cohort data when incidence is known.
  • Influence functions and bootstrap provide reliable variance estimation and confidence bands.

Conclusions:

  • The study provides versatile methods for decision curve analysis, catering to different data types and assumptions.
  • The proposed methods enhance the reliability of risk model evaluation and comparison.
  • These techniques are valuable for assessing clinical utility and guiding decision-making in various healthcare settings.