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Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level

Karla Hemming1, Richard Lilford, Alan J Girling

  • 1Department of Public Health, Epidemiology and Biostatistics, University of Birmingham, U.K.

Statistics in Medicine
|October 28, 2014
PubMed
Summary
This summary is machine-generated.

Stepped-wedge cluster randomised trials (SW-CRTs) offer flexible designs for health service evaluation. Variations like incomplete data collection and multiple clustering levels impact statistical power, requiring careful consideration in study planning.

Keywords:
clustermultiple levels of clusteringsample sizestepped-wedge

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Clinical Trial Design

Background:

  • Stepped-wedge cluster randomised trials (SW-CRTs) are increasingly utilized for health service evaluations.
  • Traditional SW-CRTs feature equally spaced steps, consistent cluster randomization, and continuous data collection.
  • This study explores variations on the standard SW-CRT design.

Purpose of the Study:

  • To introduce and analyze variations of the stepped-wedge cluster randomised trial (SW-CRT) design.
  • To investigate the implications of these design modifications on statistical power and detectable differences.
  • To provide a unified framework for comparing the efficiency of SW-CRTs and parallel cluster randomised trials (CRTs).

Main Methods:

  • Introduced incomplete cross-sectional SW-CRTs, including designs with varying cluster numbers per step and periods without data collection.
  • Considered parallel CRTs with staggered or balanced randomization and baseline measures as special cases of incomplete SW-CRTs.
  • Extended designs to accommodate multiple layers of clustering (e.g., wards within hospitals) and derived power using generalized linear mixed models.

Main Results:

  • Demonstrated that incomplete SW-CRTs can encompass parallel CRTs with staggered or baseline measures.
  • Showcased designs with multiple clustering layers and derived power and detectable difference using Wald tests.
  • Found that while transition periods may have a small impact on power, their inclusion is recommended; power can increase with higher intra-cluster correlation (ICC).

Conclusions:

  • The impact of transition periods on SW-CRT power is likely minimal but should be incorporated if part of the design.
  • The influence of the intra-cluster correlation (ICC) on power is generally smaller in SW-CRTs than in parallel CRTs, especially with multiple clustering levels.
  • The unified framework allows for efficient comparison between SW-CRT and parallel CRT designs.