Geographic pair matching in large-scale cluster randomized trials

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, Christine P. Stewart, Clair Null, Stephen P. Luby, John M. Colford, Alan E. Hubbard and Jade Benjamin-Chung
Additional contact information
Benjamin F. Arnold: 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
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

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-45152-y

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