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Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials.

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Summary

This study introduces optimal design methods for cluster randomized crossover and stepped wedge trials, crucial for efficient healthcare research. It provides algorithms and SAS macros to determine the best cluster numbers and sizes for maximum treatment effect estimation efficiency.

Keywords:
Cluster randomized crossover trialMaxiMin optimal designgeneralized estimating equationslocal optimal designrelative efficiencystepped wedge cluster randomized trial

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Area of Science:

  • Biostatistics
  • Health Services Research
  • Implementation Science

Background:

  • Longitudinal cluster randomized trials, including cluster randomized crossover (CRC) and stepped wedge cluster randomized trials (SW-CRT), are vital in healthcare delivery and implementation science.
  • While methods exist to estimate treatment effects in CRC and SW-CRT, guidance on optimal designs for maximum efficiency is limited.

Purpose of the Study:

  • To develop optimal design strategies for multiple-period CRC and SW-CRT with continuous outcomes.
  • To identify optimal cluster-period size and number of clusters for maximum efficiency under budget constraints.

Main Methods:

  • Development of local optimal design algorithms assuming known correlation parameters.
  • Proposal of MaxiMin optimal design algorithms for unknown correlation parameters, using constrained optimization for integer estimates.
  • Derivation of closed-form formulae for local and MaxiMin optimal designs in CRC trials.
  • Development of four SAS macros for practical implementation.

Main Results:

  • Optimal design algorithms and closed-form formulae were derived for CRC and SW-CRT.
  • The study addresses both closed-cohort and repeated cross-sectional sampling schemes.
  • Constrained optimization techniques were uniquely applied to obtain integer estimates for MaxiMin optimal designs.

Conclusions:

  • The developed methods and algorithms provide crucial guidance for efficient design of CRC and SW-CRT.
  • The SAS macros facilitate practical application of these optimal design strategies in research.
  • This work enhances the efficiency of treatment effect estimation in complex longitudinal cluster randomized trials.