Your browser doesn't support javascript.
loading
Geographic pair-matching in large-scale cluster randomized trials.
Arnold, Benjamin F; Rerolle, Francois; Tedijanto, Christine; Njenga, Sammy M; Rahman, Mahbubur; Ercumen, Ayse; Mertens, Andrew; Pickering, Amy; Lin, Audrie; Arnold, Charles D; Das, Kishor; Stewart, Christine P; Null, Clair; Luby, Stephen P; Colford, John M; Hubbard, Alan E; Benjamin-Chung, Jade.
Afiliação
  • Arnold BF; Francis I. Proctor Foundation, University of California, San Francisco, CA, USA.
  • Rerolle F; Department of Ophthalmology, University of California, San Francisco, CA, USA.
  • Tedijanto C; Francis I. Proctor Foundation, University of California, San Francisco, CA, USA.
  • Njenga SM; Francis I. Proctor Foundation, University of California, San Francisco, CA, USA.
  • Rahman M; Eastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute, Nairobi, Kenya.
  • Ercumen A; Environmental Interventions Unit, Infectious Diseases Division, icddr,b, Dhaka, Bangladesh.
  • Mertens A; Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA.
  • Pickering A; Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
  • Lin A; Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
  • Arnold CD; Chan Zuckerberg Biohub, San Francisco, CA.
  • Das K; Department of Biobehavioral Health, Pennsylvania State University, University Park, PA, USA.
  • Stewart CP; Department of Nutrition, University of California, Davis, CA.
  • Null C; School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland.
  • Luby SP; Department of Nutrition, University of California, Davis, CA.
  • Colford JM; Mathematica, Washington, DC, USA.
  • Hubbard AE; Infectious diseases and Geographic Medicine, Stanford University, Stanford, California.
  • Benjamin-Chung J; Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
medRxiv ; 2023 May 23.
Article em En | MEDLINE | ID: mdl-37205361
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials Idioma: En Revista: MedRxiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials Idioma: En Revista: MedRxiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos