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Sample size calculation for comparing two ROC curves.

Sin-Ho Jung1

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

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|July 12, 2024
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Summary
This summary is machine-generated.

This study introduces a new statistical test and sample size calculation for comparing two continuous-valued biomarkers used in personalized medicine. The methods accurately control error rates and maintain statistical power for biomarker performance evaluation.

Keywords:
AUCbiomarkerlocation‐shift modelprevalencesensitivityspecificity

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Area of Science:

  • Biostatistics
  • Personalized Medicine
  • Diagnostic Test Evaluation

Background:

  • Biomarkers are crucial for personalized medicine, aiding in disease status determination.
  • Continuous-valued biomarkers are often assessed using the Area Under the Curve (AUC) from receiver operating characteristic curves.
  • Comparing the performance of two biomarkers is a common research objective.

Purpose of the Study:

  • To propose a simple non-parametric statistical test for comparing the AUC of two continuous-valued biomarkers.
  • To develop a straightforward sample size calculation method for the proposed statistical test.

Main Methods:

  • A non-parametric statistical test was developed for comparing two biomarkers' AUC.
  • A sample size formula was derived, requiring AUC values, case prevalence, type I error rate, and power.
  • Simulations were conducted to evaluate the test's performance and the sample size formula's accuracy.

Main Results:

  • The proposed statistical test accurately controls the type I error rate.
  • The developed sample size calculation method effectively maintains the specified statistical power.
  • The methods are applicable when comparing two continuous biomarkers for disease status.

Conclusions:

  • The introduced statistical test and sample size calculation provide a simple and effective tool for comparing biomarker performance.
  • These methods support the rigorous evaluation of biomarkers in personalized medicine research.
  • Accurate sample size determination is essential for reliable biomarker comparison studies.