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Impact of correlation structure on sample size requirements of statistical methods for multiple binary outcomes: A

Kanako Fuyama1, Kentaro Sakamaki2, Kohei Uemura3

  • 1Department of Biostatistics, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.

Clinical Trials (London, England)
|January 3, 2025
PubMed
Summary

Understanding outcome correlations is crucial for selecting statistical methods in clinical trials. Prioritized outcome approaches often offer higher power and smaller sample sizes, especially when correlations are consistent across treatment arms.

Keywords:
Binary endpointscomposite endpointscorrelated endpointsmultiple-testing proceduresnet benefitprioritized outcomes

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Randomized clinical trials increasingly use complex methods for multiple binary outcomes.
  • Outcome correlations impact sample size requirements, but their effect on statistical power and sample size needs further investigation.

Purpose of the Study:

  • To compare the power and sample sizes of different statistical methods for analyzing multiple binary outcomes under varying correlation structures.
  • To evaluate co-primary endpoints, composite endpoints, and prioritized outcome approaches.

Main Methods:

  • Simulations were used to assess statistical power and sample size requirements.
  • Methods were evaluated across different correlations, marginal proportions, treatment effects, and numbers of outcomes.
  • A case study involving a migraine treatment trial was conducted for sample size analysis.

Main Results:

  • Correlations significantly influenced the power and sample size of composite endpoints.
  • Co-primary endpoints showed stable power across correlations but declined with opposing treatment effects or >2 components.
  • Prioritized outcome approaches generally yielded higher power and smaller sample sizes when correlations were similar between arms.

Conclusions:

  • Anticipating and accounting for correlations is vital when choosing statistical methods for multiple binary outcomes.
  • Co-primary endpoints are reliable for demonstrating superiority but not for balancing opposing treatment effects.
  • Generalized pairwise comparisons provide an effective alternative for prioritized outcomes, often minimizing sample size when correlations are shared.