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Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary

Lara Maleyeff1, Rui Wang1,2, Sebastien Haneuse1

  • 1Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

Statistics in Medicine
|November 17, 2023
PubMed
Summary

This study introduces new sample size methods for cluster randomized trials (CRTs) with binary outcomes. These methods help researchers detect treatment effect heterogeneity, a crucial step for robust clinical trial design.

Keywords:
Monte Carlo methodeffect modificationgeneralized linear mixed modelgroup randomized trialintracluster correlation coefficientunequal cluster sizes

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Health Services Research

Background:

  • Cluster randomized trials (CRTs) are widely used, but sample size determination for detecting treatment effect heterogeneity with binary outcomes remains a challenge.
  • Existing methods for heterogeneous treatment effects in CRTs are primarily limited to continuous outcomes, leaving a gap for binary outcomes.

Purpose of the Study:

  • To develop and validate sample size procedures for testing treatment effect heterogeneity in two-level cluster randomized trials with binary outcomes.
  • To provide practical tools for researchers planning CRTs involving binary outcomes and effect modifiers.

Main Methods:

  • Development of closed-form sample size expressions for binary effect modifiers within a generalized linear mixed model framework.
  • Implementation of a computationally efficient Monte Carlo approach for continuous effect modifiers.
  • Simulation studies to assess the accuracy of the proposed sample size methods and comparisons with existing approaches.

Main Results:

  • The proposed sample size methods accurately estimate the required sample size for detecting treatment effect heterogeneity in CRTs with binary outcomes.
  • Formulas and simulation results demonstrate the utility of the methods for various effect modifier types (binary and continuous).
  • Illustrative examples, including data from the STOP CRC trial, showcase the practical application of the developed procedures.

Conclusions:

  • The developed sample size procedures address a critical methodological gap for CRTs with binary outcomes.
  • These methods will enhance the planning and statistical power of CRTs investigating heterogeneous treatment effects.
  • The study provides valuable tools for researchers aiming to detect differential treatment effects in complex trial designs.