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Summary
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This study optimizes stepped cluster designs, including stepped-wedge designs, by analyzing mixed-effects models. The research identifies optimal intervention uptake times to maximize precision in both cross-sectional and longitudinal studies.

Keywords:
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Area of Science:

  • Statistics
  • Epidemiology
  • Clinical Trial Design

Background:

  • Stepped cluster designs introduce interventions at different times across clusters.
  • These designs include traditional parallel and newer stepped-wedge approaches.
  • Precision in these designs depends on mixed-effects models with various random effects.

Purpose of the Study:

  • To evaluate the precision of stepped cluster designs under mixed-effects models.
  • To determine optimal intervention uptake times for enhanced study efficiency.
  • To provide guidance on design choices for cross-sectional and longitudinal studies.

Main Methods:

  • Analysis using mixed-effects models incorporating fixed time and random subject/cluster effects.
  • Calculation of efficiency using a 'cluster-mean correlation' and design coefficients.
  • Development of an algorithm for optimizing uptake times and proposing hybrid designs.

Main Results:

  • The 'cluster-mean correlation' quantifies data dependency and combines cluster size and intra-cluster correlation.
  • Optimal designs balance parallel and stepped-wedge components, especially in large studies.
  • Hybrid designs are proposed for situations with uncertain cluster-mean correlation estimates.

Conclusions:

  • Optimized stepped cluster designs, particularly hybrid models, enhance statistical precision.
  • The choice of uptake times significantly impacts study efficiency.
  • The findings offer practical strategies for designing robust cluster-randomized trials.