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Network generalized estimating equations for complexly correlated data with applications to cluster randomized

Tom Chen1,2, Fan Li3,4, Rui Wang1,2

  • 1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, Massachusetts 02215, United States.

Biostatistics (Oxford, England)
|December 15, 2025
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Summary
This summary is machine-generated.

This study introduces a network-based framework using generalized estimating equations (GEE) to model complex dependencies in cluster randomized trials (CRTs). The network GEE approach and the networkGEE R package offer flexible solutions for parameter estimation in CRTs.

Keywords:
clustered datageneralized equicorrelated marginal means assumptions (GEMMA)intracluster correlationstepped wedge designstochastic optimization

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Area of Science:

  • Statistics
  • Biostatistics
  • Public Health Research

Background:

  • Estimating parameters and association structures in cluster randomized trials (CRTs) presents significant methodological challenges.
  • Existing methods struggle with complex dependency structures common in CRTs.

Purpose of the Study:

  • To introduce a novel network-based framework for estimating parameters and modeling complex association structures in CRTs.
  • To develop a flexible and computationally efficient method for analyzing CRTs with large cluster sizes.

Main Methods:

  • Leveraging network concepts to represent complex dependency structures within CRTs.
  • Utilizing generalized estimating equations (GEE) with a focus on locally exchangeable observations within partitioned groups.
  • Introducing the networkGEE R package to address computational challenges in large CRTs.

Main Results:

  • The network GEE framework demonstrates flexibility in modeling various exchangeable, moving average, and exponential decay structures.
  • Extensive simulation studies validate the proposed methods' performance.
  • The networkGEE R package enables fitting models beyond the capabilities of existing statistical software.

Conclusions:

  • The proposed network GEE framework provides a robust and flexible approach for analyzing complex dependency structures in CRTs.
  • The networkGEE R package enhances the practical application of these methods, particularly for large-scale trials.
  • This framework offers significant advancements in statistical methodologies for CRTs, with implications for public health research.