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 Jr; Alan E Hubbard; Jade Benjamin-Chung

doi:10.1038/s41467-024-45152-y

Geographic pair matching in large-scale cluster randomized trials

Nat Commun. 2024 Feb 5;15(1):1069. doi: 10.1038/s41467-024-45152-y.

Authors

Benjamin F Arnold^{1

2}, Francois Rerolle³, Christine Tedijanto³, Sammy M Njenga⁴, Mahbubur Rahman⁵, Ayse Ercumen⁶, Andrew Mertens⁷, Amy J Pickering^{8

9}, Audrie Lin¹⁰, Charles D Arnold¹¹, Kishor Das¹², Christine P Stewart¹¹, Clair Null¹³, Stephen P Luby¹⁴, John M Colford Jr⁷, Alan E Hubbard¹⁵, Jade Benjamin-Chung^{9

16}

Affiliations

¹ Francis I. Proctor Foundation, University of California, San Francisco, CA, USA. ben.arnold@ucsf.edu.
² Department of Ophthalmology, University of California, San Francisco, CA, USA. ben.arnold@ucsf.edu.
³ Francis I. Proctor Foundation, University of California, San Francisco, CA, USA.
⁴ Eastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute, Nairobi, Kenya.
⁵ Environmental Interventions Unit, Infectious Diseases Division, icddr,b, Dhaka, Bangladesh.
⁶ Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA.
⁷ Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
⁸ Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
⁹ Chan Zuckerberg Biohub, San Francisco, CA, USA.
¹⁰ Department of Biobehavioral Health, Pennsylvania State University, University Park, PA, USA.
¹¹ Department of Nutrition, University of California, Davis, CA, USA.
¹² CURAM, SFI Research Centre for Medical Devices, University of Galway, Galway, Ireland.
¹³ Mathematica, Washington, DC, USA.
¹⁴ Infectious diseases and Geographic Medicine, Stanford University, Stanford, CA, USA.
¹⁵ Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
¹⁶ Department of Epidemiology and Population Health, Stanford University, CA, USA.

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.

MeSH terms

Bangladesh
Child
Cluster Analysis
Humans
Kenya
Public Health*
Randomized Controlled Trials as Topic
Research Design*
Sample Size

Abstract

MeSH terms

Grants and funding