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Estimating transformations for evaluating diagnostic tests with covariate adjustment.

Ainesh Sewak1, Torsten Hothorn1

  • 1Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Zürich, Switzerland.

Statistical Methods in Medical Research
|June 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel regression model for receiver operating characteristic (ROC) curve analysis, addressing complexities in medical diagnostic data. The proposed method offers unbiased estimates and reliable statistical inference for evaluating diagnostic test accuracy.

Keywords:
Transformation modelYouden indexarea under the receiver operating characteristic curvecensoringdiagnostic testdistribution regressionlimit of detectionordinal outcomeoverlapping coefficientreceiver operating characteristic curve

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Modeling

Background:

  • Receiver operating characteristic (ROC) analysis is crucial for evaluating medical diagnostic tests.
  • Existing methods struggle with complex medical data, including non-normal data, covariates, ordinal biomarkers, and censored data.
  • A unified framework for consistent statistical inference in ROC analysis is lacking.

Purpose of the Study:

  • To propose a flexible regression model for ROC curve analysis that accommodates complex medical data features.
  • To provide a robust statistical framework for estimating ROC curves and summary indices.
  • To ensure consistent statistical inference in the presence of data complexities.

Main Methods:

  • A regression model for transformed test results is developed, leveraging the invariance of ROC curves to monotonic transformations.
  • The model handles non-normal data, influential covariates, ordinal biomarkers, and censored data.
  • Simulation studies are conducted to assess the performance of the proposed method.

Main Results:

  • Simulation results demonstrate that the transformation model provides unbiased estimates.
  • The method achieves coverage probabilities at nominal levels, indicating reliable statistical inference.
  • The methodology is successfully applied to a real-world cross-sectional study on metabolic syndrome.

Conclusions:

  • The proposed transformation model offers a robust and flexible approach to ROC analysis for complex medical data.
  • This methodology enhances the evaluation and comparison of diagnostic test accuracy.
  • Software implementation is available in the R package 'tram', facilitating broader application.