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Geographic pair matching in large-scale cluster randomized trials

Author

Listed:
  • Benjamin F. Arnold

    (University of California
    University of California)

  • Francois Rerolle

    (University of California)

  • Christine Tedijanto

    (University of California)

  • Sammy M. Njenga

    (Kenya Medical Research Institute)

  • Mahbubur Rahman

    (Infectious Diseases Division, icddr,b)

  • Ayse Ercumen

    (North Carolina State University)

  • Andrew Mertens

    (University of California)

  • Amy J. Pickering

    (University of California
    Chan Zuckerberg Biohub)

  • Audrie Lin

    (Pennsylvania State University)

  • Charles D. Arnold

    (University of California)

  • Kishor Das

    (University of Galway)

  • Christine P. Stewart

    (University of California)

  • Clair Null

    (Mathematica)

  • Stephen P. Luby

    (Stanford University)

  • John M. Colford

    (University of California)

  • Alan E. Hubbard

    (University of California)

  • Jade Benjamin-Chung

    (Chan Zuckerberg Biohub
    Department of Epidemiology and Population Health)

Abstract

Cluster randomized trials are often used to study large-scale public health interventions. In large trials, even small improvements in statistical efficiency can have profound impacts on the required sample size and cost. Location integrates many socio-demographic and environmental characteristics into a single, readily available feature. Here we show that pair matching by geographic location leads to substantial gains in statistical efficiency for 14 child health outcomes that span growth, development, and infectious disease through a re-analysis of two large-scale trials of nutritional and environmental interventions in Bangladesh and Kenya. Relative efficiencies from pair matching are ≥1.1 for all outcomes and regularly exceed 2.0, meaning an unmatched trial would need to enroll at least twice as many clusters to achieve the same level of precision as the geographically pair matched design. We also show that geographically pair matched designs enable estimation of fine-scale, spatially varying effect heterogeneity under minimal assumptions. Our results demonstrate broad, substantial benefits of geographic pair matching in large-scale, cluster randomized trials.

Suggested Citation

  • Benjamin F. Arnold & Francois Rerolle & Christine Tedijanto & Sammy M. Njenga & Mahbubur Rahman & Ayse Ercumen & Andrew Mertens & Amy J. Pickering & Audrie Lin & Charles D. Arnold & Kishor Das & Chris, 2024. "Geographic pair matching in large-scale cluster randomized trials," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45152-y
    DOI: 10.1038/s41467-024-45152-y
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    References listed on IDEAS

    as
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