|
1
|
Arnold BF, Rerolle F, Tedijanto C, Njenga SM, Rahman M, Ercumen A, Mertens A, Pickering AJ, Lin A, Arnold CD, Das K, Stewart CP, Null C, Luby SP, Colford JM, Hubbard AE, Benjamin-Chung J. Geographic pair matching in large-scale cluster randomized trials. Nat Commun 2024; 15:1069. [PMID: 38316755 PMCID: PMC10844220 DOI: 10.1038/s41467-024-45152-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 01/17/2024] [Indexed: 02/07/2024] Open
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.
Collapse
Affiliation(s)
- Benjamin F Arnold
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA.
- Department of Ophthalmology, University of California, San Francisco, CA, USA.
| | - Francois Rerolle
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Christine Tedijanto
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Sammy M Njenga
- Eastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute, Nairobi, Kenya
| | - Mahbubur Rahman
- Environmental Interventions Unit, Infectious Diseases Division, icddr,b, Dhaka, Bangladesh
| | - Ayse Ercumen
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA
| | - Andrew Mertens
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA
| | - Amy J Pickering
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Audrie Lin
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA, USA
| | - Charles D Arnold
- Department of Nutrition, University of California, Davis, CA, USA
| | - Kishor Das
- CURAM, SFI Research Centre for Medical Devices, University of Galway, Galway, Ireland
| | | | | | - Stephen P Luby
- Infectious diseases and Geographic Medicine, Stanford University, Stanford, CA, USA
| | - John M Colford
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA
| | - Alan E Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
| | - Jade Benjamin-Chung
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Epidemiology and Population Health, Stanford University, CA, USA
| |
Collapse
|
|
2
|
Arnold BF, Rerolle F, Tedijanto C, Njenga SM, Rahman M, Ercumen A, Mertens A, Pickering A, Lin A, Arnold CD, Das K, Stewart CP, Null C, Luby SP, Colford JM, Hubbard AE, Benjamin-Chung J. Geographic pair-matching in large-scale cluster randomized trials. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.30.23289317. [PMID: 37205361 PMCID: PMC10187339 DOI: 10.1101/2023.04.30.23289317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Custer 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. Pair matched randomization is one strategy with potential to increase trial efficiency, but to our knowledge there have been no empirical evaluations of pair-matching in large-scale, epidemiologic field trials. Location integrates many socio-demographic and environmental characteristics into a single feature. Here, we show that geographic pair-matching 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. We estimate relative efficiencies ≥1.1 for all outcomes assessed and relative efficiencies regularly exceed 2.0, meaning an unmatched trial would have needed 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.
Collapse
Affiliation(s)
- Benjamin F. Arnold
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Ophthalmology, University of California, San Francisco, CA, USA
| | - Francois Rerolle
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Christine Tedijanto
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Sammy M. Njenga
- Eastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute, Nairobi, Kenya
| | - Mahbubur Rahman
- Environmental Interventions Unit, Infectious Diseases Division, icddr,b, Dhaka, Bangladesh
| | - Ayse Ercumen
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA
| | - Andrew Mertens
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA
| | - Amy Pickering
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA
| | - Audrie Lin
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA, USA
| | | | - Kishor Das
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
| | | | | | - Stephen P. Luby
- Infectious diseases and Geographic Medicine, Stanford University, Stanford, California
| | - John M. Colford
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA
| | - Alan E. Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
| | - Jade Benjamin-Chung
- Chan Zuckerberg Biohub, San Francisco, CA
- Department of Epidemiology and Population Health, Stanford University, CA, USA
| |
Collapse
|