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Sample size calculator for cluster randomized trials.

Marion K Campbell1, Sean Thomson, Craig R Ramsay

  • 1Health Services Research Unit, University of Aberdeen, Polwarth Building Foresterhill, Aberdeen, Scotland AB25 2ZD UK. m.k.campbell@abdn.ac.uk

Computers in Biology and Medicine
|February 20, 2004
PubMed
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Cluster randomized trials require larger sample sizes due to correlated outcomes within groups. This study presents a calculator to adjust sample sizes by increasing cluster size or number of clusters for healthcare evaluations.

Area of Science:

  • Health research methodology
  • Biostatistics
  • Clinical trial design

Background:

  • Cluster randomized trials are increasingly prevalent in healthcare evaluations.
  • Clustered designs necessitate adjustments in study design, conduct, and analysis.
  • Standard sample size calculations must account for within-cluster correlation.

Purpose of the Study:

  • To address the need for appropriate sample size calculations in cluster randomized trials.
  • To provide a tool for researchers to determine adequate sample sizes for clustered studies.
  • To illustrate the impact of increasing cluster size versus the number of clusters on sample size inflation.

Main Methods:

  • Development of a sample size calculator specifically for cluster randomized trials.

Related Experiment Videos

  • Inclusion of options to adjust sample size by modifying cluster size.
  • Inclusion of options to adjust sample size by modifying the number of clusters.

    • Main Results:

    • The presented calculator facilitates sample size determination for cluster trials.
    • It allows for the evaluation of trade-offs between cluster size and number of clusters.
    • Demonstrates methods for inflating sample sizes to account for intra-cluster correlation.

    Conclusions:

    • Accurate sample size calculation is crucial for the validity of cluster randomized trials.
    • The developed calculator aids researchers in optimizing study design for clustered data.
    • Understanding the impact of cluster size and number is key for efficient trial planning.