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Power analysis for cluster randomized trials with continuous coprimary endpoints.

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Summary

New methods enable accurate sample size and power calculations for cluster randomized trials (CRTs) with multiple continuous endpoints. This addresses a gap in pragmatic trial design, improving the reliability of health care intervention evaluations.

Keywords:
coefficient of variationgeneral linear hypothesisintersection-union testmultivariate linear mixed modelsample size determinationunequal cluster size

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Health Services Research

Background:

  • Pragmatic trials frequently use cluster randomization (CRTs) for logistical or scientific reasons.
  • Coprimary endpoints are common in CRTs but often overlooked in sample size and power calculations.
  • Existing power analysis methods for CRTs primarily address binary endpoints, leaving a gap for continuous outcomes.

Purpose of the Study:

  • To develop methods for sample size and power calculations for CRTs with multiple continuous coprimary endpoints.
  • To provide a statistical framework for handling complex correlation structures within clusters.
  • To extend existing methods to accommodate unequal cluster sizes.

Main Methods:

  • Derivation of the closed-form joint distribution for K treatment effect estimators using a multivariate linear mixed model (MLMM).
  • Characterization of the relationship between statistical power and various intraclass correlation coefficients.
  • Approximation of the joint distribution under unequal cluster sizes using cluster size mean and coefficient of variation.

Main Results:

  • The proposed method accurately predicts statistical power for CRTs with continuous coprimary endpoints.
  • The derived methods account for multiple intraclass correlation types within the MLMM framework.
  • Simulations demonstrate good agreement between predicted and empirical power, even with estimated MLMM parameters.

Conclusions:

  • The developed methods provide a robust approach for sample size and power determination in CRTs with continuous coprimary endpoints.
  • This work addresses a critical need in pragmatic trial design, enhancing the statistical rigor of health care intervention studies.
  • The approach is applicable to real-world CRTs and facilitates more reliable evaluation of interventions.