Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations
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...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61502-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849764029368631296 |
|---|---|
| author | 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 Von Seidlein Mavuto Mukaka Jacklin Mosha Natacha Protopopoff Manfred Accrombessi Richard Hayes Thomas S. Churcher Jackie Cook |
| author_facet | 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 Von Seidlein Mavuto Mukaka Jacklin Mosha Natacha Protopopoff Manfred Accrombessi Richard Hayes Thomas S. Churcher Jackie Cook |
| author_sort | Joseph Biggs |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9922eb2d0e03478e94ea8a8b7ba71c80 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9922eb2d0e03478e94ea8a8b7ba71c802025-08-20T03:05:14ZengNature PortfolioNature Communications2041-17232025-07-0116111310.1038/s41467-025-61502-wCharacterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculationsJoseph Biggs0Joseph D. Challenger1Dominic Dee2Eldo Elobolobo3Carlos Chaccour4Francisco Saute5Sarah G. Staedke6Sibo Vilakati7Jade Benjamin Chung8Michelle S. Hsiang9Edgard Diniba Dabira10Annette Erhart11Umberto D’Alessandro12Rupam Tripura13Thomas J. Peto14Lorenz Von Seidlein15Mavuto Mukaka16Jacklin Mosha17Natacha Protopopoff18Manfred Accrombessi19Richard Hayes20Thomas S. Churcher21Jackie Cook22International Statistics and Epidemiology Group, Department of Infectious Disease Epidemiology and International Health, London School of Hygiene and Tropical Medicine (LSHTM)Medical research Council (MRC) Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College LondonMedical research Council (MRC) Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College LondonCentro de Investigaçao em Saúde de ManhiçaBarcelona Institute for Global Health (ISGlobal)Centro de Investigaçao em Saúde de ManhiçaDepartment of Vector Biology, Liverpool School of Tropical MedicineNational Malaria Control Programme, Ministry of HealthDepartment of Epidemiology and Population Health, Stanford UniversityChan Zuckerberg BiohubMedical Research Council (MRC) Unit The Gambia at the London School of Hygiene and Tropical Medicine (LSHTM), Disease Control & Elimination ThemeMedical Research Council (MRC) Unit The Gambia at the London School of Hygiene and Tropical Medicine (LSHTM), Disease Control & Elimination ThemeMedical Research Council (MRC) Unit The Gambia at the London School of Hygiene and Tropical Medicine (LSHTM), Disease Control & Elimination ThemeMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol UniversityMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol UniversityMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol UniversityMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol UniversityDepartment of Parasitology, National Institute for Medical ResearchDepartment of Epidemiology and Public Health, Swiss Tropical & Public Health InstitutePopulation Services International (PSI), Malaria departmentInternational Statistics and Epidemiology Group, Department of Infectious Disease Epidemiology and International Health, London School of Hygiene and Tropical Medicine (LSHTM)Medical research Council (MRC) Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College LondonInternational Statistics and Epidemiology Group, Department of Infectious Disease Epidemiology and International Health, London School of Hygiene and Tropical Medicine (LSHTM)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.https://doi.org/10.1038/s41467-025-61502-w |
| spellingShingle | 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 Von Seidlein Mavuto Mukaka Jacklin Mosha Natacha Protopopoff Manfred Accrombessi Richard Hayes Thomas S. Churcher Jackie Cook Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations Nature Communications |
| title | Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations |
| title_full | Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations |
| title_fullStr | Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations |
| title_full_unstemmed | Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations |
| title_short | Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations |
| title_sort | characterisation of between cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations |
| url | https://doi.org/10.1038/s41467-025-61502-w |
| work_keys_str_mv | AT josephbiggs characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT josephdchallenger characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT dominicdee characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT eldoelobolobo characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT carloschaccour characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT franciscosaute characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT sarahgstaedke characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT sibovilakati characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT jadebenjaminchung characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT michelleshsiang characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT edgarddinibadabira characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT annetteerhart characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT umbertodalessandro characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT rupamtripura characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT thomasjpeto characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT lorenzvonseidlein characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT mavutomukaka characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT jacklinmosha characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT natachaprotopopoff characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT manfredaccrombessi characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT richardhayes characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT thomasschurcher characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations AT jackiecook characterisationofbetweenclusterheterogeneityinmalariaclusterrandomisedtrialstoinformfuturesamplesizecalculations |