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Related Concept Videos

Bootstrapping01:24

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Estimating negative likelihood ratio confidence when test sensitivity is 100%: A bootstrapping approach.

Keith A Marill1, Yuchiao Chang2, Kim F Wong3

  • 11 Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.

Statistical Methods in Medical Research
|July 9, 2015
PubMed
Summary

Estimating confidence intervals for likelihood ratios with 100% sensitivity is challenging. A new bootstrapping method provides more realistic estimates for negative likelihood ratio confidence intervals, especially in critical care diagnostics.

Keywords:
Monte Carlo methodSensitivity and specificitybiostatisticsbootstrappingconfidence intervalsdata interpretationstatistical

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Area of Science:

  • Medical Diagnostics
  • Biostatistics

Background:

  • High-sensitivity diagnostic tests are vital in emergency and critical care.
  • Estimating confidence intervals (CIs) for likelihood ratios (LRs) is difficult when sample sensitivity reaches 100%.

Purpose of the Study:

  • To develop, compare, and automate a bootstrapping method for estimating the negative LR CI when sample sensitivity is 100%.

Main Methods:

  • A novel bootstrapping approach was developed using binomial distribution to find the lowest population sensitivity yielding 100% sample sensitivity.
  • Simulations assessed CI coverage of the true negative LR.
  • Comparisons were made with existing methods including individual extremes, Gart and Nam, and Score CI.

Main Results:

  • The bootstrapping method demonstrated appropriate coverage of the nominal 95% CI across various scenarios.
  • For a sample with 100% sensitivity, bootstrapping yielded narrower CIs (e.g., 0-0.048) compared to other methods (e.g., 0-0.073).
  • The R package "bootLR" automates this process.

Conclusions:

  • Existing methods may produce overly wide negative LR CIs when sample sensitivity is 100%.
  • The developed bootstrapping approach offers a more realistic and easily implemented solution.
  • This methodology is applicable to other binomial proportions with homogeneous responses.