IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-45152-y.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-45152-y
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-45152-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Varnell, S.P. & Murray, D.M. & Janega, J.B. & Blitstein, J.L., 2004. "Design and Analysis of Group-Randomized Trials: A Review of Recent Practices," American Journal of Public Health, American Public Health Association, vol. 94(3), pages 393-399.
    2. Gary King & Emmanuela Gakidou & Nirmala Ravishankar & Ryan T. Moore & Jason Lakin & Manett Vargas & Martha María Téllez-Rojo & Juan Eugenio Hernández Ávila & Mauricio Hernández Ávila & Héctor Hernánde, 2007. "A “politically robust” experimental design for public policy evaluation, with application to the Mexican Universal Health Insurance program," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 26(3), pages 479-506.
    3. Donner, A. & Klar, N., 2004. "Pitfalls of and Controversies in Cluster Randomization Trials," American Journal of Public Health, American Public Health Association, vol. 94(3), pages 416-422.
    4. Murray, D.M. & Varnell, S.P. & Blitstein, J.L., 2004. "Design and Analysis of Group-Randomized Trials: A Review of Recent Methodological Developments," American Journal of Public Health, American Public Health Association, vol. 94(3), pages 423-432.
    5. Peter J. Diggle & Emanuele Giorgi, 2016. "Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1096-1120, July.
    6. Small, Dylan S. & Ten Have, Thomas R. & Rosenbaum, Paul R., 2008. "Randomization Inference in a GroupRandomized Trial of Treatments for Depression: Covariate Adjustment, Noncompliance, and Quantile Effects," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 271-279, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jonathan L. Blitstein & David M. Murray & Peter J. Hannan & William R. Shadish, 2005. "Increasing the Degrees of Freedom in Future Group Randomized Trials," Evaluation Review, , vol. 29(3), pages 268-286, June.
    2. Jonathan L. Blitstein & Peter J. Hannan & David M. Murray & William R. Shadish, 2005. "Increasing the Degrees of Freedom in Existing Group Randomized Trials," Evaluation Review, , vol. 29(3), pages 241-267, June.
    3. Ji-Hyun Lee & Michael J Schell & Richard Roetzheim, 2009. "Analysis of Group Randomized Trials with Multiple Binary Endpoints and Small Number of Groups," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-9, October.
    4. repec:mpr:mprres:4632 is not listed on IDEAS
    5. Peter Z. Schochet, "undated". "Statistical Power for Random Assignment Evaluations of Education Programs," Mathematica Policy Research Reports 6749d31ad72d4acf988f7dce5, Mathematica Policy Research.
    6. Ruoxuan Xiong & Susan Athey & Mohsen Bayati & Guido Imbens, 2019. "Optimal Experimental Design for Staggered Rollouts," Papers 1911.03764, arXiv.org, revised Sep 2023.
    7. Steven Teerenstra & Bing Lu & John S. Preisser & Theo van Achterberg & George F. Borm, 2010. "Sample Size Considerations for GEE Analyses of Three-Level Cluster Randomized Trials," Biometrics, The International Biometric Society, vol. 66(4), pages 1230-1237, December.
    8. Winfried Zinn & Sebastian Sauer & Richard Göllner, 2016. "The German Inpatient Satisfaction Scale," SAGE Open, , vol. 6(2), pages 21582440166, April.
    9. Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, American Economic Association, vol. 16(1), pages 193-212, January.
    10. Azuara, Oliver, 2011. "Effect of universal health coverage on marriage, cohabitation and labor force participation," MPRA Paper 35074, University Library of Munich, Germany.
    11. Sun, Xiaojie & Liu, Xiaoyun & Sun, Qiang & Yip, Winnie & Wagstaff, Adam & Meng, Qingyue, 2014. "The impact of a pay-for-performance scheme on prescription quality in rural China : an impact evaluation," Policy Research Working Paper Series 6892, The World Bank.
    12. Fitzsimons, Emla & Malde, Bansi & Mesnard, Alice & Vera-Hernández, Marcos, 2016. "Nutrition, information and household behavior: Experimental evidence from Malawi," Journal of Development Economics, Elsevier, vol. 122(C), pages 113-126.
    13. Rebecca L. Thornton & Laurel E. Hatt & Erica M. Field & Mursaleena Islam & Freddy Solís Diaz & Martha Azucena González, 2010. "Social security health insurance for the informal sector in Nicaragua: a randomized evaluation," Health Economics, John Wiley & Sons, Ltd., vol. 19(S1), pages 181-206, September.
    14. Sophie Song and Katsushi S. Imai, 2018. "Does the Hunger Safety Net Programme Reduce Multidimensional Poverty? Evidence from Kenya," OPHI Working Papers ophiwp124.pdf, Queen Elizabeth House, University of Oxford.
    15. Thomas Barrios & Rebecca Diamond & Guido W. Imbens & Michal Kolesár, 2012. "Clustering, Spatial Correlations, and Randomization Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 578-591, June.
    16. Omar Galárraga & Sandra Sosa-Rubí & Aarón Salinas-Rodríguez & Sergio Sesma-Vázquez, 2010. "Health insurance for the poor: impact on catastrophic and out-of-pocket health expenditures in Mexico," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(5), pages 437-447, October.
    17. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.
    18. M. Taha Kasim & Benjamin Ukert, 2021. "The impact of WIC participation on tobacco use and alcohol consumption," Contemporary Economic Policy, Western Economic Association International, vol. 39(3), pages 608-625, July.
    19. Labonne, Julien, 2013. "The local electoral impacts of conditional cash transfers," Journal of Development Economics, Elsevier, vol. 104(C), pages 73-88.
    20. Azuara, Oliver & Marinescu, Ioana, 2011. "Informality and the expansion of social protection programs," MPRA Paper 35073, University Library of Munich, Germany.
    21. Faraz Usmani & Marc Jeuland & Subhrendu K. Pattanayak, 2018. "NGOs and the effectiveness of interventions," WIDER Working Paper Series wp-2018-59, World Institute for Development Economic Research (UNU-WIDER).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45152-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.