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Sample size calculation for stepped wedge and other longitudinal cluster randomised trials.

Richard Hooper1, Steven Teerenstra2, Esther de Hoop3

  • 1Centre for Primary Care & Public Health, Queen Mary University of London, London, U.K.. r.l.hooper@qmul.ac.uk.

Statistics in Medicine
|June 29, 2016
PubMed
Summary

Calculating sample size for longitudinal cluster randomized trials requires accounting for multiple levels of clustering. New formulas address this complexity, but may underestimate needs with few clusters, emphasizing accurate parameter estimation.

Keywords:
clinical trial designcluster randomised trialintracluster correlationsample sizestepped wedge

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology

Background:

  • Cluster randomized trials (CRTs) require larger sample sizes than individually randomized trials due to intracluster correlation.
  • Longitudinal CRTs, including stepped wedge designs, necessitate accounting for clustering at both the cluster and time levels.

Purpose of the Study:

  • To derive sample size calculation formulas for longitudinal CRTs with normally distributed outcomes.
  • To develop a multilevel model accommodating variations between clusters and over time within clusters.

Main Methods:

  • Derivation of sample size formulas for repeated cross-section and closed cohort longitudinal CRTs.
  • Utilizing a multilevel model to account for cluster and time effects.
  • Estimation of nuisance parameters including intracluster correlation and cluster/individual autocorrelation.

Main Results:

  • Formulas derived align with existing methods for specific designs (e.g., crossover, ANCOVA).
  • Simulations indicate potential underestimation of sample size when the number of clusters is small.
  • Accurate estimation of nuisance parameters is crucial for reliable sample size calculations.

Conclusions:

  • The derived formulas provide a framework for sample size calculations in longitudinal CRTs.
  • Careful estimation of intracluster and temporal correlations is essential for accurate sample size determination.
  • Further validation through simulation is recommended, particularly for scenarios with limited clusters.