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Cost-efficient designs of cluster unit trials

S M McKinlay1

  • 1New England Research Institutes, Inc., Watertown, Massachusetts 02172.

Preventive Medicine
|September 1, 1994
PubMed
Summary

Cohort designs are more cost-efficient for short cluster unit trials with high autocorrelation. This finding aids in planning cost-effective complex intervention studies.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Public Health Interventions

Background:

  • Large cluster randomized trials are essential for evaluating complex interventions in settings like communities and schools.
  • Designing these trials involves choosing between cohort or cross-sectional sampling within clusters, with cost being a key consideration.
  • Optimizing the balance between precision and cost is crucial for efficient trial design.

Purpose of the Study:

  • To develop and apply an algorithm for optimizing the design of cluster randomized trials based on cost-efficiency.
  • To compare the cost-effectiveness of cohort versus cross-sectional sampling designs in cluster trials.
  • To provide a framework for selecting the optimal number of clusters and observations per cluster.

Main Methods:

  • Utilized a unified model for analyzing large cluster unit trials (Feldman & McKinlay, 1993).
  • Integrated the variance of the treatment effect with a cost function to create an optimization algorithm.
  • Contrasted cohort designs (following individuals over time) with cross-sectional designs (independent samples at each time point).

Main Results:

  • The developed algorithm identified optimal trial designs balancing precision and cost.
  • Cohort designs were found to be more cost-efficient than cross-sectional designs under specific conditions.
  • Cost-efficiency favored cohort designs in shorter trials and those with high autocorrelation (>= 0.75).

Conclusions:

  • The algorithm effectively aids in designing cost-efficient cluster unit trials.
  • Accessible estimates of variance and cost components from previous trials are vital for future planning.
  • This approach supports more economical and precise evaluation of complex interventions.

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