Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations

Joseph Biggs (), Joseph D. Challenger, Dominic Dee, Eldo Elobolobo, Carlos Chaccour, Francisco Saute, Sarah G. Staedke, Sibo Vilakati, Jade Benjamin Chung, Michelle S. Hsiang, Edgard Diniba Dabira, Annette Erhart, Umberto D’Alessandro, Rupam Tripura, Thomas J. Peto, Lorenz Seidlein, Mavuto Mukaka, Jacklin Mosha, Natacha Protopopoff, Manfred Accrombessi, Richard Hayes, Thomas S. Churcher and Jackie Cook
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Joseph Biggs: London School of Hygiene and Tropical Medicine (LSHTM)
Joseph D. Challenger: Imperial College London
Dominic Dee: Imperial College London
Eldo Elobolobo: Centro de Investigaçao em Saúde de Manhiça
Carlos Chaccour: Barcelona Institute for Global Health (ISGlobal)
Francisco Saute: Centro de Investigaçao em Saúde de Manhiça
Sarah G. Staedke: Liverpool School of Tropical Medicine
Sibo Vilakati: Ministry of Health
Jade Benjamin Chung: Stanford University
Michelle S. Hsiang: Chan Zuckerberg Biohub
Edgard Diniba Dabira: Disease Control & Elimination Theme
Annette Erhart: Disease Control & Elimination Theme
Umberto D’Alessandro: Disease Control & Elimination Theme
Rupam Tripura: Mahidol University
Thomas J. Peto: Mahidol University
Lorenz Seidlein: Mahidol University
Mavuto Mukaka: Mahidol University
Jacklin Mosha: National Institute for Medical Research
Natacha Protopopoff: Swiss Tropical & Public Health Institute
Manfred Accrombessi: Malaria department
Richard Hayes: London School of Hygiene and Tropical Medicine (LSHTM)
Thomas S. Churcher: Imperial College London
Jackie Cook: London School of Hygiene and Tropical Medicine (LSHTM)

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-61502-w

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