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Related Concept Videos

Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Methods of Medium Optimization

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Column Efficiency: Plate Theory01:10

Column Efficiency: Plate Theory

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Decision Making: P-value Method01:09

Decision Making: P-value Method

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Compacting Factor test

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Related Experiment Videos

Making every component count: Using the Shapley value to improve win ratio analysis.

Valerie Fu1

  • 1Independent Researcher, Carmel, IN, USA.

Journal of Biopharmaceutical Statistics
|July 13, 2026
PubMed
Summary

We introduce Shapley-Enhanced Win Ratio Analysis (SEWRA) to clarify how individual outcomes contribute to composite endpoints in clinical trials. SEWRA provides an additive decomposition of treatment effects, improving interpretation beyond the overall win ratio.

Keywords:
Composite endpointsSEWRA algorithmShapley valuecooperative game theoryinterpretable machine learningwin ratio

Related Experiment Videos

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Outcomes Research

Background:

  • Composite endpoints are common in clinical trials, combining multiple outcomes.
  • The win ratio method hierarchically compares patients but doesn't detail individual component contributions.
  • Interpreting partial win ratios is challenging due to their non-additive nature.

Purpose of the Study:

  • To develop a method for accurately attributing treatment effects to individual components within composite endpoints.
  • To enhance the interpretability of win ratio analyses in clinical trials.

Main Methods:

  • Proposed Shapley-Enhanced Win Ratio Analysis (SEWRA).
  • Applied Shapley value to the log win ratio over component subsets.
  • Developed methods for reporting contributions on both log and original win ratio scales.

Main Results:

  • SEWRA provides an additive decomposition of the overall log win ratio.
  • Component contributions can be reported multiplicatively on the original win ratio scale.
  • Simulations and a case study demonstrated SEWRA's ability to reveal hierarchy-induced contributions.

Conclusions:

  • SEWRA complements existing win ratio methods by offering clear attribution of treatment effects.
  • This method enhances understanding of how individual outcomes drive composite endpoint results.
  • SEWRA improves the interpretability of complex clinical trial endpoint analyses.