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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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An R-Based Landscape Validation of a Competing Risk Model
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Substantial risks associated with few clusters in cluster randomized and stepped wedge designs.

Monica Taljaard1, Steven Teerenstra2, Noah M Ivers3

  • 1Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON, Canada mtaljaard@ohri.ca.

Clinical Trials (London, England)
|March 5, 2016
PubMed
Summary

Cluster randomization trials, especially stepped wedge designs, require careful sample size calculations. Current methods may underestimate needs due to correlation assumptions, highlighting the need for further research.

Keywords:
Cluster randomized trialcluster cross-over trialsample size calculationstepped wedge trial

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Area of Science:

  • Health Services Research
  • Clinical Trials Methodology
  • Biostatistics

Background:

  • Cluster randomization designs are increasingly used in quality improvement and pragmatic trials.
  • Limited numbers of clusters are common in these studies.
  • Designs incorporating pre-intervention measurements can reduce cluster requirements.

Purpose of the Study:

  • To examine the implications of limited clusters in cluster randomization trials.
  • To address limitations in current sample size methods for stepped wedge designs.
  • To advocate for further methodological development for stepped wedge trials.

Main Methods:

  • Review of statistical methods for cluster randomization designs, including stepped wedge and cluster cross-over.
  • Analysis of sample size calculation assumptions regarding correlation structures.
  • Identification of risks associated with small numbers of clusters.

Main Results:

  • Pre-intervention measurements can reduce the number of clusters needed.
  • Few clusters increase risks of chance imbalances and statistical errors.
  • Current stepped wedge sample size methods assume equal intracluster and inter-period correlations, potentially leading to underestimated sample sizes.

Conclusions:

  • Stepped wedge designs require careful consideration of correlation structures for accurate sample size estimation.
  • Methodological advancements are needed for stepped wedge trials to ensure adequate power and generalizability.
  • Further research should establish minimum thresholds for stepped wedge design implementation.