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Nearest-Neighbor Estimation for ROC Analysis under Verification Bias.

Gianfranco Adimari, Monica Chiogna

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    This study introduces a new nonparametric method to accurately estimate receiver operating characteristic (ROC) curves for diagnostic tests, addressing biases caused by incomplete disease status verification. The method improves accuracy without relying on potentially incorrect statistical models.

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    Area of Science:

    • Biostatistics
    • Medical Diagnostics
    • Statistical Modeling

    Background:

    • Receiver operating characteristic (ROC) curves are essential for evaluating continuous diagnostic tests.
    • Verification bias arises when disease status is confirmed for only a subset of subjects, leading to biased ROC curve estimation.
    • Existing bias correction methods often rely on parametric models, risking inconsistency due to model misspecification.

    Purpose of the Study:

    • To propose a fully nonparametric method for estimating ROC curves in the presence of verification bias.
    • To overcome limitations of existing parametric approaches by avoiding model misspecification issues.
    • To provide a robust estimation technique for diagnostic test performance evaluation.

    Main Methods:

    • Developed a nonparametric approach based on nearest-neighbor imputation.
    • Employed generic smooth regression models for disease probability and verification probability.
    • The method addresses verification bias without assuming missing at random (MAR) or specific parametric forms.

    Main Results:

    • The proposed nonparametric method effectively corrects for verification bias in ROC curve estimation.
    • Simulation experiments demonstrate the method's usefulness and robustness compared to existing techniques.
    • Illustrative examples confirm the practical applicability of the new approach.

    Conclusions:

    • The fully nonparametric method offers a reliable alternative for estimating ROC curves under verification bias.
    • This approach mitigates risks associated with parametric model misspecification in diagnostic accuracy studies.
    • The method provides a valuable tool for accurate diagnostic test evaluation in complex verification scenarios.