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  1. Home
  3. Beyond The Fragility Index.
  1. Home
  3. Beyond The Fragility Index.

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Fragility Assessment of Bovine Cortical Bone Using Scratch Tests
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Beyond the Fragility Index.

Piero Quatto1,2,3, Enrico Ripamonti2,4, Donata Marasini3

  • 1Department of Economics, Management and Statistics, University of Milan-Bicocca, Milan, Italy.

Pharmaceutical Statistics
|November 22, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

The fragility index (FI) has limitations in analyzing randomized clinical trials (RCTs). This study introduces a new "analysis of strength" (AS) with a strength index (SI) to provide a more robust statistical assessment beyond p-values.

Keywords:
fragility indexlikelihood ratiorandomized clinical trialstrength index

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Area of Science:

  • Statistics
  • Clinical Trials
  • Biostatistics

Background:

  • Randomized clinical trials (RCTs) often use the fragility index (FI) to assess results.
  • The FI has known weaknesses and limitations in complementing p-value information.
  • A need exists for a more comprehensive statistical approach to evaluate trial outcomes.

Purpose of the Study:

  • To introduce a novel framework called "analysis of strength" (AS).
  • To propose a new "strength index" (SI) as an alternative to the FI.
  • To extend statistical evidence assessment beyond p-values using likelihood ratios.

Main Methods:

  • Developed a new strength index (SI) for normal distribution settings, applicable to both significant and nonsignificant results.
  • Extended the analysis of strength to time-to-event outcomes.
  • Utilized likelihood ratios for statistical evidence, building on Royall's viewpoint.
  • Created an R package for strength calculations and conducted simulation studies.

    • Main Results:

    • The proposed strength index (SI) offers advantages over the FI, including a threshold.
    • The likelihood-based approach provides a complementary statistical evidence measure.
    • Simulations demonstrated the empirical behavior of SI and the likelihood-based indicator.
    • The AS framework was applied to three COVID-19 treatment trials.

    Conclusions:

    • The analysis of strength (AS) offers a more reliable complement to p-values than the fragility index (FI).
    • The new strength index (SI) and likelihood-based methods provide robust statistical evidence.
    • This approach enhances the interpretation of results from randomized clinical trials, including those for COVID-19 treatments.