Evidence map›Paper›PMID 28024952›Full record

ArticleVaccine2017

Real-time dynamic modelling for the design of a cluster-randomized phase 3 Ebola vaccine trial in Sierra Leone.

A Camacho, R M Eggo, N Goeyvaerts, A Vandebosch, R Mogg, S Funk, A J Kucharski, C H Watson, T Vangeneugden, W J Edmunds

Open access · hybridAbstract read
PubMed Publisher
In one paragraph

Article in Vaccine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

13 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  4. A prospective prediction tool for understanding Crimean-Congo haemorrhagic fever dynamics in Turkey.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020
    Article
  5. Article
  6. Outbreak analytics: a developing data science for informing the response to emerging pathogens.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 3 countries.

A CamachoDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
R M EggoDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK. Electronic address: r.eggo@lshtm.ac.uk.
N GoeyvaertsJanssen Research & Development, Beerse, Belgium.
A VandeboschJanssen Research & Development, Beerse, Belgium.
R MoggJanssen Research & Development, LLC, Spring House, PA, USA.
S FunkDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
A J KucharskiDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
C H WatsonDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
T VangeneugdenJanssen Research & Development, Beerse, Belgium.
W J EdmundsDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
University of London · GBJanssen (Belgium) · BEJanssen (United States) · US

Funding

Medical Research Council MR/K021680/1
6 · The paper itself

Abstract

backgroundDeclining incidence and spatial heterogeneity complicated the design of phase 3 Ebola vaccine trials during the tail of the 2013-16 Ebola virus disease (EVD) epidemic in West Africa. Mathematical models can provide forecasts of expected incidence through time and can account for both vaccine efficacy in participants and effectiveness in populations. Determining expected disease incidence was critical to calculating power and determining trial sample size.

methodsIn real-time, we fitted, forecasted, and simulated a proposed phase 3 cluster-randomized vaccine trial for a prime-boost EVD vaccine in three candidate regions in Sierra Leone. The aim was to forecast trial feasibility in these areas through time and guide study design planning.

resultsEVD incidence was highly variable during the epidemic, especially in the declining phase. Delays in trial start date were expected to greatly reduce the ability to discern an effect, particularly as a trial with an effective vaccine would cause the epidemic to go extinct more quickly in the vaccine arm. Real-time updates of the model allowed decision-makers to determine how trial feasibility changed with time.

conclusionsThis analysis was useful for vaccine trial planning because we simulated effectiveness as well as efficacy, which is possible with a dynamic transmission model. It contributed to decisions on choice of trial location and feasibility of the trial. Transmission models should be utilised as early as possible in the design process to provide mechanistic estimates of expected incidence, with which decisions about sample size, location, timing, and feasibility can be determined.

Indexed as

Clinical Trials, Phase III as TopicDisease Transmission, InfectiousEbola VaccinesHemorrhagic Fever, EbolaHumansIncidenceModels, StatisticalSierra LeoneEbola VaccinesCollaborationEbola vaccineEpidemicPhase 3Transmission modellingTrial design

Identifiers

PMID28024952
OpenAlexW2563666499

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.