Evidence map›Paper›PMID 37205361›Full record

ArticlemedRxiv : the preprint server for health sciences2023

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 Pickering, Audrie Lin, Charles D Arnold and 7 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Benjamin F ArnoldFrancis I. Proctor Foundation, University of California, San Francisco, CA, USA.
Francois RerolleFrancis I. Proctor Foundation, University of California, San Francisco, CA, USA.
Christine TedijantoFrancis I. Proctor Foundation, University of California, San Francisco, CA, USA.
Sammy M NjengaEastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute, Nairobi, Kenya.
Mahbubur RahmanEnvironmental Interventions Unit, Infectious Diseases Division, icddr,b, Dhaka, Bangladesh.
Ayse ErcumenDepartment of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA.
Andrew MertensDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
Amy PickeringDepartment of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
Audrie LinDepartment of Biobehavioral Health, Pennsylvania State University, University Park, PA, USA.
Charles D ArnoldDepartment of Nutrition, University of California, Davis, CA.
Kishor DasSchool of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland.
Christine P StewartDepartment of Nutrition, University of California, Davis, CA.
Clair NullMathematica, Washington, DC, USA.
Stephen P LubyInfectious diseases and Geographic Medicine, Stanford University, Stanford, California.
John M ColfordDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
Alan E HubbardDivision of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
Jade Benjamin-ChungChan Zuckerberg Biohub, San Francisco, CA.

Funding

Serologic measures of enteric pathogen transmission for intervention studies and population monitoring in low-resource settingsR01AI166671 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Benjamin F Arnold · 2022 to 2026
$2.7M
Measuring spillover effects of reactive, focal malaria elimination interventionsK01AI141616 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI BENJAMIN-CHUNG, JADE · 2019 to 2022
$601k
NIAID NIH HHS K01 AI141616NIAID NIH HHS R01 AI166671
6 · The paper itself

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.

Identifiers

PMID37205361
PMCPMC10187339

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.