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Sample size determination for external pilot cluster randomised trials with binary feasibility outcomes: a tutorial.

K Hemming1, M Taljaard2,3, E Gkini4

  • 1Institute of Applied Health Research, University of Birmingham, Birmingham, UK. k.hemming@bham.ac.uk.

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Summary
This summary is machine-generated.

Justifying sample size for pilot trials requires specific methods, not standard effectiveness testing. This study provides guidance for pilot cluster trials, focusing on feasibility outcomes and offering practical tools for researchers.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Services Research

Background:

  • Pilot trials require sample size justification, but current methods are often misapplied.
  • Conventional sample size approaches are unsuitable for pilot trials, which focus on feasibility, not effectiveness.
  • Feasibility objectives, often binary, inform sample size for subsequent full-scale trials.

Purpose of the Study:

  • To demonstrate sample size justification for external pilot cluster trials estimating binary feasibility outcomes.
  • To provide practical tools and formulae for sample size calculations in this context.
  • To report on intra-cluster correlation coefficients for feasibility outcomes.

Main Methods:

  • Development of sample size calculation formulae for various scenarios in pilot cluster trials.
  • Creation of an R Shiny application for implementing the proposed sample size methods.
  • Compilation of intra-cluster correlation coefficients for feasibility outcomes from existing data.

Main Results:

  • Sample size calculations for pilot cluster trials depend on the number of clusters, cluster sizes, and intra-cluster correlation coefficients (ICC) for feasibility outcomes.
  • Feasibility outcomes may exhibit larger ICCs than clinical outcomes, with limited existing data.
  • Efficiency in external pilot cluster trials can be improved by increasing the number of clusters and decreasing observations per cluster, unless ICCs are very low.

Conclusions:

  • This tutorial offers a framework for justifying sample sizes in pilot cluster trials focused on binary feasibility outcomes.
  • The provided R Shiny app facilitates the practical application of these sample size methodologies.
  • Understanding ICCs for feasibility outcomes is crucial for optimizing pilot cluster trial design and efficiency.