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Efficient design of geographically-defined clusters with spatial autocorrelation.

Samuel I Watson1

  • 1University of Birmingham, Birmingham, UK.

Journal of Applied Statistics
|October 10, 2022
PubMed
Summary

This study introduces efficient methods for designing geographically defined clusters in research. It optimizes cluster design parameters to improve efficiency and reduce costs in survey and experimental studies.

Keywords:
Samplingcluster randomised trialpowerspatial

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Area of Science:

  • Spatial statistics
  • Survey methodology
  • Biostatistics

Background:

  • Cluster-based designs are common in research but can be inefficient due to within-cluster correlation.
  • Current cluster design often relies on arbitrary parameter choices and lacks sensitivity to design variations.

Purpose of the Study:

  • To develop efficient methods for designing geographically defined clusters.
  • To optimize cluster design parameters for improved efficiency and cost-effectiveness in research studies.

Main Methods:

  • Utilized geostatistical models for spatial autocorrelation to approximate within-cluster covariance.
  • Estimated effective sample size based on cluster design parameters.
  • Examined the impact of factors like number of locations, cluster area, and sampling proportion on design efficiency.

Main Results:

  • Demonstrated how geographical cluster design parameters influence efficiency.
  • Provided a framework for optimizing cluster design under budget constraints.
  • Showcased simple interpretations of model parameters for practical application in design analysis.

Conclusions:

  • Geostatistical modeling offers a robust approach to designing efficient, geographically defined clusters.
  • Optimized cluster design can enhance the cost-effectiveness of survey and experimental studies.
  • The proposed methods provide practical tools for researchers to improve study design.