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Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
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ROC curve estimation under test-result-dependent sampling.

Xiaofei Wang1, Junling Ma, Stephen L George

  • 1Department of Biostatistics & Bioinformatics, Duke University Medical Center, Durham, NC 27710, USA. xiaofei.wang@duke.edu

Biostatistics (Oxford, England)
|June 23, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a biased sampling method to improve the precision of receiver operating characteristic (ROC) curve estimation for biomarkers. The empirical likelihood method effectively analyzes this data, offering better efficiency than alternatives.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Clinical Trials

Background:

  • Receiver operating characteristic (ROC) curves are crucial for evaluating continuous biomarkers in disease prediction.
  • Traditional study designs can be inefficient and costly.
  • Novel designs are needed for improved estimation efficiency and reduced study costs.

Purpose of the Study:

  • To introduce a biased sampling scheme combining a simple random sample (SRC) and a tailored data collection (TDC) for enhanced ROC curve estimation.
  • To address the bias introduced by test-result-dependent sampling in biomarker accuracy assessment.
  • To propose and evaluate analytical methods, particularly empirical likelihood, for analyzing such data structures.

Main Methods:

  • A biased sampling scheme involving SRC and TDC to over/undersample subjects based on biomarker values.
  • Development and application of three analytical approaches for test-result-dependent data.
  • Focus on the empirical likelihood method for estimating covariate-specific and covariate-independent ROC curves.
  • Establishment of asymptotic properties and variance estimators for empirical likelihood estimators.

Main Results:

  • The empirical likelihood method demonstrates good statistical properties and superior efficiency compared to alternative methods in simulation studies.
  • The proposed biased sampling scheme improves ROC curve estimation precision for a fixed sample size.
  • Recommendations for optimal sampling strategies (number of regions, cutoff points, subject allocation) are provided.

Conclusions:

  • The empirical likelihood method is a robust and efficient approach for analyzing ROC curve data obtained from biased sampling schemes.
  • The proposed methods offer a cost-effective and precise alternative for biomarker performance evaluation in clinical research.
  • The findings are validated through simulations and a lung cancer clinical trial data example.