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A class of two-sample nonparametric statistics for binary and time-to-event outcomes.

Marta Bofill Roig1,2, Guadalupe Gómez Melis1

  • 1Department of Statistics and Operations Research, 16767Universitat Politècnica de Catalunya, Barcelona, Spain.

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This study introduces novel non-parametric statistical tests for comparing proportions and survival functions, offering a flexible approach for clinical trial analysis without assuming proportional hazards.

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Clinical trialsmixed outcomesmultiple endpointsnon-proportional hazardssurvival analysisweighted Mean survival test

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trials

Background:

  • Comparing proportions and survival functions is crucial in clinical research.
  • Existing methods often rely on assumptions like proportional hazards, limiting their applicability.
  • There is a need for flexible, non-parametric statistical tools.

Purpose of the Study:

  • To propose a new class of two-sample statistics for testing equality of proportions and survival functions.
  • To develop a non-parametric method that does not require the proportional hazards assumption.
  • To provide a flexible framework for analyzing clinical trial data.

Main Methods:

  • A weighted combination of a score test for proportions and a Kaplan-Meier statistic for survival functions.
  • Development of asymptotic distributions and variance estimators for the proposed statistics.
  • Evaluation of performance using simulation studies and a real-world cancer vaccine trial.

Main Results:

  • The proposed statistics are fully non-parametric and do not assume proportional hazards.
  • Asymptotic properties are established under fixed and local alternatives.
  • The method's performance is validated in small sample sizes and a Phase III trial.

Conclusions:

  • The novel statistics offer a robust and flexible approach for comparing proportions and survival functions.
  • The R package SurvBin facilitates the application of these methods in biostatistical analysis.
  • This work enhances the analytical toolkit for clinical trial data, particularly in oncology.