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Using the potential outcome framework to estimate optimal sample size for cluster randomized trials: a

Ruoshui Zhai1, Roee Gutman1

  • 1Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA.

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|December 3, 2021
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Summary
This summary is machine-generated.

This study introduces a new Monte Carlo method for sample size calculations in cluster randomized trials (CRTs). The approach enhances accuracy and flexibility for diverse study designs and resource constraints.

Keywords:
causal estimandcluster randomized trialspotential outcomes frameworksample size estimation

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Cluster randomized trials (CRTs) involve group-level randomization, leading to intra-cluster correlation that complicates sample size calculations.
  • Existing methods for CRT sample size estimation often rely on asymptotic formulas or standard Monte Carlo simulations.
  • Accurate sample size determination is crucial for achieving adequate statistical power in CRTs.

Purpose of the Study:

  • To propose a novel Monte Carlo procedure for sample size estimation in CRTs based on the potential outcomes framework.
  • To develop a flexible method applicable to various study designs, including finite and infinite populations.
  • To facilitate sample size calculations that account for practical constraints like unequal cluster allocation.

Main Methods:

  • A new Monte Carlo procedure grounded in the potential outcomes framework was developed.
  • The method explicitly defines causal estimands, data generation, sampling, and treatment assignment mechanisms.
  • The procedure accommodates unequal cluster allocation for financial and administrative considerations.

Main Results:

  • The proposed Monte Carlo procedure offers a robust approach to sample size calculation in CRTs.
  • It provides a more comprehensive framework compared to traditional asymptotic methods.
  • The method is adaptable to complex study designs and population types.

Conclusions:

  • The novel Monte Carlo procedure enhances the accuracy and applicability of sample size calculations in cluster randomized trials.
  • The potential outcomes framework provides a rigorous foundation for addressing complexities in CRT design.
  • The associated R package, CRTsampleSearch, facilitates the implementation of this advanced methodology.