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Estimation of AUC or Partial AUC under Test-Result-Dependent Sampling.

Xiaofei Wang1, Junling Ma, Stephen George

  • 1Department of Biostatistics & Bioinformatics, Duke University Medical Center, DUMC 2717, Durham, N.C. 27710, U.S.A.

Statistics in Biopharmaceutical Research
|February 9, 2013
PubMed
Summary
This summary is machine-generated.

We introduce a test-result-dependent sampling (TDS) scheme for efficient biomarker validation. This method improves accuracy estimates for the area under the ROC curve (AUC) and partial area under the ROC curve (pAUC) compared to simple random sampling.

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Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Clinical Trials

Background:

  • Area under the ROC curve (AUC) and partial AUC (pAUC) assess biomarker accuracy.
  • Standard sampling in biomarker validation is inefficient and costly, especially for invasive disease status determination.

Purpose of the Study:

  • To propose a test-result-dependent sampling (TDS) scheme for efficient biomarker validation.
  • To develop consistent nonparametric estimators for AUC and pAUC under TDS.
  • To evaluate the efficiency of TDS compared to simple random sampling (SRS).

Main Methods:

  • Estimating the test-result distribution from TDS data.
  • Developing and establishing asymptotic properties of nonparametric AUC and pAUC estimators.
  • Conducting simulation studies to compare TDS with SRS.

Main Results:

  • The proposed TDS design yields more efficient AUC and pAUC estimates than SRS.
  • Simulation studies demonstrate good finite sample properties of the estimators.
  • A cancer clinical trial example illustrates the TDS design and estimators.

Conclusions:

  • The TDS scheme offers a more efficient and cost-effective approach for biomarker validation studies.
  • The proposed estimators provide reliable AUC and pAUC estimates.
  • This methodology has broad applications in biomarker validation design and analysis.