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Significance, Fragility, and Robustness in Clinical Trials: Stratifying Statistical Evidence.

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Summary

The p-fr-nb framework, assessing statistical significance, fragility, and robustness, offers a more complete view of clinical trial evidence than p-values alone. This validation in binary-outcome trials shows it effectively stratifies evidence quality for better interpretation.

Keywords:
clinical trial methodologyfragility indexmodified arm fragility quotientmonte carlo simulationneutrality boundary frameworkrisk quotientstatistical robustnesstrial evidence quality

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Evidence-Based Medicine

Background:

  • Current reporting standards for clinical trials often rely solely on p-values, effect sizes, and confidence intervals, providing only partial evidence.
  • These metrics quantify significance and magnitude but fail to capture classification stability (fragility) and distance from therapeutic neutrality (robustness).
  • The p-fr-nb framework, incorporating p-value, fragility, and robustness, aims to provide a more comprehensive assessment of statistical evidence.

Purpose of the Study:

  • To validate the p-fr-nb framework in two-arm, binary-outcome clinical trials.
  • To assess the framework's ability to stratify evidence quality beyond traditional statistical measures.
  • To evaluate the prevalence of concordant-positive (CP) and significant-fragile-weak (SFW) patterns in empirical trials compared to simulations.

Main Methods:

  • A pragmatic observational study analyzed 129 two-arm, binary-outcome clinical trials from PubMed across 15 specialties.
  • Null expectations were generated using Monte Carlo simulations of 720,000 trials, including 120,000 null trials (relative risk = 1.0).
  • Fragility was measured by the modified-arm fragility quotient (MFQ), and robustness by the risk quotient (RQ); CP and SFW criteria were defined.

Main Results:

  • Among 77 statistically significant empirical trials, 39.0% met the concordant-positive (CP) criteria for stability and robustness.
  • Conversely, 31.2% of significant trials exhibited the significant-fragile-weak (SFW) pattern, indicating fragile significance and minimal separation from no effect.
  • Null simulations showed CP triplets in only 1.4% of significant trials, highlighting the framework's ability to distinguish robust findings.

Conclusions:

  • The p-fr-nb framework effectively stratifies evidence quality in clinical trials, providing insights beyond p-values and confidence intervals.
  • A substantial proportion of statistically significant findings lacked stability and robustness, as indicated by the SFW pattern.
  • Incorporating fragility and robustness metrics enhances the assessment of evidence heterogeneity, crucial for reproducibility and clinical interpretation.