Evidence map›Paper›PMID 25886798›Full record

ArticleThe Lancet. Infectious diseases2015

Statistical power and validity of Ebola vaccine trials in Sierra Leone: a simulation study of trial design and analysis.

Steven E Bellan, Juliet R C Pulliam, Carl A B Pearson, David Champredon, Spencer J Fox, Laura Skrip, Alison P Galvani, Manoj Gambhir, Ben A Lopman, Travis C Porco and 2 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Infectious diseases, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
8.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 8 institutions in 3 countries.

Steven E BellanCenter for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX, USA. Electronic address: steve.bellan@gmail.com.
Juliet R C PulliamDepartment of Biology, University of Florida, Gainesville, FL, USA; Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
Carl A B PearsonEmerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
David ChampredonSchool of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada.
Spencer J FoxDepartment of Integrative Biology, The University of Texas at Austin, Austin, TX, USA.
Laura SkripCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, CT, USA.
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, CT, USA; Department of Ecology and Evolution, Yale University, New Haven, CT, USA.
Manoj GambhirDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; IHRC Inc, Atlanta, GA, USA.
Ben A LopmanDivision of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Travis C PorcoFrancis I Proctor Foundation, University of California, San Francisco, CA, USA; Department of Ophthalmology, University of California, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA.
Lauren Ancel MeyersDepartment of Integrative Biology, The University of Texas at Austin, Austin, TX, USA; The Santa Fe Institute, Santa Fe, NM, USA.
Jonathan DushoffDepartment of Biology, McMaster University, Hamilton, ON, Canada.
McMaster University · CAThe University of Texas at Austin · USUniversity of Florida · USYale University · USNational Center for Emerging and Zoonotic Infectious Diseases · USNational Center for Immunization and Respiratory Diseases · USSan Francisco Foundation · USSanta Fe Institute · US

Funding

Impacts of Individual and Social Behavior on Influenza Dynamics and ControlU01GM087719 · NIGMS · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2009 to 2018
$6.3M
"Modeling contact investigation and rapid response"U01GM087728 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PORCO, TRAVIS CHRISTIAN · 2011 to 2015
$2.2M
International Clinics on Infectious Disease Dynamics and Data (ICI3D Program)R25GM102149 · NIGMS · UNIVERSITY OF FLORIDA · PI PULLIAM, JULIET RC, WELTE, ALEX · 2012 to 2015
$696k
Canadian Institutes of Health ResearchNIGMS NIH HHS R25 GM102149NIGMS NIH HHS R25GM102149NIGMS NIH HHS U01 GM087719NIGMS NIH HHS U01GM087719NIGMS NIH HHS U01 GM087728NIGMS NIH HHS U01GM087728
6 · The paper itself

Abstract

backgroundSafe and effective vaccines could help to end the ongoing Ebola virus disease epidemic in parts of west Africa, and mitigate future outbreaks of the virus. We assess the statistical validity and power of randomised controlled trial (RCT) and stepped-wedge cluster trial (SWCT) designs in Sierra Leone, where the incidence of Ebola virus disease is spatiotemporally heterogeneous, and is decreasing rapidly.

methodsWe projected district-level Ebola virus disease incidence for the next 6 months, using a stochastic model fitted to data from Sierra Leone. We then simulated RCT and SWCT designs in trial populations comprising geographically distinct clusters at high risk, taking into account realistic logistical constraints, and both individual-level and cluster-level variations in risk. We assessed false-positive rates and power for parametric and non-parametric analyses of simulated trial data, across a range of vaccine efficacies and trial start dates.

findingsFor an SWCT, regional variation in Ebola virus disease incidence trends produced increased false-positive rates (up to 0·15 at α=0·05) under standard statistical models, but not when analysed by a permutation test, whereas analyses of RCTs remained statistically valid under all models. With the assumption of a 6-month trial starting on Feb 18, 2015, we estimate the power to detect a 90% effective vaccine to be between 49% and 89% for an RCT, and between 6% and 26% for an SWCT, depending on the Ebola virus disease incidence within the trial population. We estimate that a 1-month delay in trial initiation will reduce the power of the RCT by 20% and that of the SWCT by 49%.

interpretationSpatiotemporal variation in infection risk undermines the statistical power of the SWCT. This variation also undercuts the SWCT's expected ethical advantages over the RCT, because an RCT, but not an SWCT, can prioritise vaccination of high-risk clusters.

fundingUS National Institutes of Health, US National Science Foundation, and Canadian Institutes of Health Research.

Indexed as

Randomized Controlled Trials as TopicBiomedical ResearchBiostatisticsEbola VaccinesHemorrhagic Fever, EbolaHumansSierra LeoneEbola Vaccines

Identifiers

PMID25886798
PMCPMC4815262
OpenAlexW2167006957

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.