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Power analysis for cluster randomized trials with multiple binary co-primary endpoints.

Dateng Li1, Jing Cao1, Song Zhang2

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Summary
This summary is machine-generated.

This study introduces a new power analysis method for cluster randomized trials (CRTs) with multiple binary co-primary endpoints. The method, based on generalized estimating equations, accurately assesses statistical power and type I error, crucial for complex clinical trial designs.

Keywords:
binarycluster randomized trialsmultiple co-primary endpointspower analysissample size

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Public Health Research

Background:

  • Cluster randomized trials (CRTs) are increasingly complex, often evaluating multiple co-primary endpoints due to advances in medical therapies and outcome monitoring.
  • Existing power analysis methods may not adequately address the complexities of CRTs with multiple co-primary endpoints.

Purpose of the Study:

  • To develop and present a novel power analysis method for cluster randomized trials (CRTs) involving multiple binary co-primary endpoints.
  • To provide a robust framework for evaluating statistical power and type I error in such complex trial designs.

Main Methods:

  • The proposed method utilizes the generalized estimating equation (GEE) approach.
  • It incorporates three types of correlations: inter-subject within endpoints, intra-subject across endpoints, and inter-subject across endpoints.
  • A closed-form joint distribution for K test statistics is derived to facilitate hypothesis testing.

Main Results:

  • The derived joint distribution enables precise evaluation of power and type I error for various hypotheses.
  • A theorem is presented that elucidates the relationship between different correlation types and testing power.
  • Extensive simulation studies confirm the performance of the proposed power analysis method.

Conclusions:

  • The developed power analysis method offers a reliable tool for designing and analyzing CRTs with multiple binary co-primary endpoints.
  • This approach enhances the accuracy of statistical power and type I error assessment in complex public health and medical research.
  • The findings are validated through simulations and demonstrated with a real-world clinical trial application.