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Benefit-risk assessment for binary diagnostic tests.

Tianyu Bai1, Lan Huang2, Meijuan Li2

  • 1Department of Management Science and Statistics, University of Texas at San Antonio , San Antonio , Texas , USA.

Journal of Biopharmaceutical Statistics
|September 10, 2019
PubMed
Summary

We propose a novel benefit-risk (BR) measure for diagnostic devices, incorporating correct and incorrect diagnoses. This measure aids decision-making in clinical studies, offering a balanced view of diagnostic accuracy.

Keywords:
Binary diagnostic testbenefit-risk assessmentbootstrapdelta method

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Area of Science:

  • Medical Device Evaluation
  • Biostatistics
  • Clinical Trial Design

Background:

  • Evaluating diagnostic devices requires a distinct benefit-risk (BR) assessment compared to therapeutic devices.
  • Accurate diagnosis is crucial for timely treatment, while errors can lead to adverse outcomes like unnecessary procedures or incorrect therapies.
  • Traditional accuracy metrics (sensitivity, specificity, PPV, NPV) do not fully capture the integrated BR profile.

Purpose of the Study:

  • To introduce a new BR measure for diagnostic devices that integrates information on both correct and incorrect diagnoses.
  • To develop statistical models for estimating this BR measure under different sampling schemes (cross-sectional, case-control).
  • To provide methods for constructing confidence intervals (CIs) for the proposed BR measure.

Main Methods:

  • Proposed a novel BR measure incorporating true-positive, true-negative, false-positive, and false-negative cases.
  • Developed statistical models for estimating the BR measure using maximum likelihood estimators (MLEs).
  • Constructed confidence intervals (CIs) using asymptotic normality of MLEs and parametric bootstrap re-sampling.
  • Evaluated CI performance via Monte Carlo simulations and applied the methodology to clinical trial data.

Main Results:

  • The proposed BR measure effectively incorporates diagnostic accuracy components for decision-making.
  • Statistical models were developed for estimating the BR measure under various sampling conditions.
  • Confidence intervals constructed using both asymptotic normality and bootstrap methods demonstrated good performance in simulations.
  • The methodology was successfully applied to real-world clinical trial datasets.

Conclusions:

  • The developed BR measure provides a comprehensive approach to evaluating diagnostic devices.
  • The proposed statistical methods and CIs are reliable for estimating and assessing the uncertainty of the BR measure.
  • This framework supports informed decision-making in the evaluation of diagnostic technologies.