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Weighted win loss approach for analyzing prioritized outcomes.

Xiaodong Luo1, Junshan Qiu2, Steven Bai2

  • 1Research and Development, Sanofi US, Bridgewater, 08807, NJ, U.S.A.

Statistics in Medicine
|March 27, 2017
PubMed
Summary

The win loss approach is enhanced with weighted statistics for analyzing prioritized outcomes, improving efficiency and interpretation in time-to-event analyses.

Keywords:
clinical trialscomposite end pointscontribution indexprioritized outcomesvariance estimationweighted win ratio

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Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Survival Analysis

Background:

  • The win loss approach, proposed by Buyse (2010) and Pocock et al. (2012), is used for analyzing prioritized outcomes.
  • Traditional survival analysis focuses on time to the first event, but may not fully capture complex prioritized outcomes.

Purpose of the Study:

  • To investigate the relationship between the win loss approach and traditional survival analysis for time to first event.
  • To propose weighted win loss statistics for improved efficiency over unweighted methods.
  • To develop methods for hypothesis testing, study design, and result interpretation in win loss analyses.

Main Methods:

  • Comparative analysis of win loss approach and traditional survival analysis.
  • Development and derivation of closed-form variance estimators for weighted win loss statistics.
  • Calculation of a contribution index for enhanced result interpretation.
  • Simulation studies and real data analysis to evaluate proposed statistics.

Main Results:

  • The relationship between win loss and survival analysis for time to first event was elucidated.
  • Weighted win loss statistics demonstrated improved efficiency compared to unweighted methods.
  • A closed-form variance estimator was successfully derived, facilitating statistical inference.
  • The contribution index provided a clearer interpretation of weighted win loss results.

Conclusions:

  • The proposed weighted win loss statistics offer an efficient and interpretable method for analyzing prioritized outcomes in time-to-event data.
  • The derived variance estimator supports robust hypothesis testing and study design.
  • The contribution index enhances the practical application of the win loss approach in biostatistical research.