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powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities.

François Grolleau1, Robert Tibshirani2,2, Jonathan H Chen1,3,4

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Accurate sample size is vital for validating prediction models using the area under the receiver operating characteristic curve (AUROC). This study introduces powerROC, a tool to simplify sample size calculations for AUROC validation, ensuring reliable model generalizability.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Clinical Epidemiology

Background:

  • External validation is essential for assessing prediction model generalizability, often using discrimination metrics like AUROC.
  • Current sample size calculations for AUROC-based validation studies frequently lack rigor, leading to underpowered analyses.
  • Reliable sample size determination is critical for accurate external validation and trustworthy clinical predictions.

Purpose of the Study:

  • To review fundamental concepts for precise sample size determination in AUROC-based external validation.
  • To enhance accessibility of sample size calculation theory and practice for researchers and clinicians.
  • To introduce powerROC, an open-source tool for simplifying sample size calculations in model validation.

Main Methods:

  • Review of statistical concepts for sample size determination in AUROC validation.
  • Introduction of powerROC, a web tool for calculating sample sizes for single and dual model comparisons.
  • Demonstration of powerROC using a hospital mortality prediction case study with the MIMIC database.

Main Results:

  • powerROC facilitates sample size calculations for AUROC validation, accommodating single model evaluation and two-model comparisons.
  • The tool supports flexible approaches, utilizing pilot data or user-defined probability distributions.
  • Case study illustrates the practical application of powerROC for assessing prediction model performance.

Conclusions:

  • Accurate sample size determination is crucial for robust external validation of prediction models using AUROC.
  • The powerROC tool simplifies complex calculations, promoting more reliable and accessible sample size planning.
  • Improved sample size methodology enhances the generalizability and trustworthiness of clinical prediction models.