Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations
1
Issued Date
2025-12-01
Resource Type
eISSN
20411723
Scopus ID
2-s2.0-105011061810
Journal Title
Nature Communications
Volume
16
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Nature Communications Vol.16 No.1 (2025)
Suggested Citation
Biggs J., Challenger J.D., Dee D., Elobolobo E., Chaccour C., Saute F., Staedke S.G., Vilakati S., Chung J.B., Hsiang M.S., Dabira E.D., Erhart A., D’Alessandro U., Tripura R., Peto T.J., Von Seidlein L., Mukaka M., Mosha J., Protopopoff N., Accrombessi M., Hayes R., Churcher T.S., Cook J. Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations. Nature Communications Vol.16 No.1 (2025). doi:10.1038/s41467-025-61502-w Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111396
Title
Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations
Author's Affiliation
Stanford University
University of California, San Francisco
UCSF School of Medicine
Universität Basel
London School of Hygiene & Tropical Medicine
Universidad de Navarra
Imperial College Faculty of Medicine
Nuffield Department of Medicine
Liverpool School of Tropical Medicine
Swiss Tropical and Public Health Institute Swiss TPH
Kenya Medical Research Institute
Instituto de Salud Global de Barcelona
Mahidol Oxford Tropical Medicine Research Unit
Centro de Investigación Biomédica en Red de Enfermedades Infecciosas
Chan Zuckerberg Biohub
National Institute for Medical Research Tanga
Centro de Investigação em Saúde de Manhiça CISM
Population Services International
Clinical Research department
Ministry of Health
University of California, San Francisco
UCSF School of Medicine
Universität Basel
London School of Hygiene & Tropical Medicine
Universidad de Navarra
Imperial College Faculty of Medicine
Nuffield Department of Medicine
Liverpool School of Tropical Medicine
Swiss Tropical and Public Health Institute Swiss TPH
Kenya Medical Research Institute
Instituto de Salud Global de Barcelona
Mahidol Oxford Tropical Medicine Research Unit
Centro de Investigación Biomédica en Red de Enfermedades Infecciosas
Chan Zuckerberg Biohub
National Institute for Medical Research Tanga
Centro de Investigação em Saúde de Manhiça CISM
Population Services International
Clinical Research department
Ministry of Health
Corresponding Author(s)
Other Contributor(s)
Abstract
Cluster randomised trials (CRTs) are important tools for evaluating the community-wide effect of malaria interventions. During the design stage, CRT sample sizes need to be inflated to account for the cluster heterogeneity in measured outcomes. The coefficient of variation (k), a measure of such heterogeneity, is typically used in malaria CRTs yet is often predicted without prior data. Underestimation of k decreases study power, thus increases the probability of generating null results. In this meta-analysis of cluster-summary data from 24 malaria CRTs, we calculate true prevalence and incidence k values using methods-of-moments and regression modelling approaches. Using random effects regression modelling, we investigate the impact of empirical k values on original trial power and explore factors associated with elevated k. Results show empirical estimates of k often exceed those used in sample size calculations, which reduces study power and effect size precision. Elevated k values are associated with incidence outcomes (compared to prevalence), lower endemicity settings, and uneven intervention coverage across clusters. Study findings can enhance the robustness of future malaria CRT sample size calculations by providing informed k estimates based on expected prevalence or incidence, in the absence of cluster-level data.