title: Estimating the probability of demonstrating vaccine efficacy in the declining Ebola epidemic: a Bayesian modelling approach.
abstract: OBJECTIVE
  We investigate the chance of demonstrating Ebola vaccine efficacy in an individually randomised controlled trial implemented in the declining epidemic of Forécariah prefecture, Guinea.
  
  
  METHODS
  We extend a previously published dynamic transmission model to include a simulated individually randomised controlled trial of 100,000 participants. Using Bayesian methods, we fit the model to Ebola case incidence before a trial and forecast the expected dynamics until disease elimination. We simulate trials under these forecasts and test potential start dates and rollout schemes to assess power to detect efficacy, and bias in vaccine efficacy estimates that may be introduced.
  
  
  RESULTS
  Under realistic assumptions, we found that a trial of 100,000 participants starting after 1 August had less than 5% chance of having enough cases to detect vaccine efficacy. In particular, gradual recruitment precludes detection of vaccine efficacy because the epidemic is likely to go extinct before enough participants are recruited. Exclusion of early cases in either arm of the trial creates bias in vaccine efficacy estimates.
  
  
  CONCLUSIONS
  The very low Ebola virus disease incidence in Forécariah prefecture means any individually randomised controlled trial implemented there is unlikely to be successful, unless there is a substantial increase in the number of cases.
authors.affiliation: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Anton
authors.identifier: http://orcid.org/0000-0002-0023-3985
authors.initials: A
authors.lastname: Camacho
authors.affiliation: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Rosalind M
authors.identifier: http://orcid.org/0000-0002-0362-6717
authors.initials: RM
authors.lastname: Eggo
authors.affiliation: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Funk
authors.affiliation: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Conall H
authors.initials: CH
authors.lastname: Watson
authors.affiliation: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Kucharski
authors.affiliation: Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: W John
authors.initials: WJ
authors.lastname: Edmunds
mesh_terms: D001499:Bayes Theorem; D058872:Epidemics; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology / Q000517:prevention & control*; D006801:Humans; D015994:Incidence; D008954:Models, Biological; D011336:Probability; D014612:Vaccines
chemical_list: D014612:Vaccines
keywords: Clinical trials < THERAPEUTICS; Protocols & guidelines < HEALTH SERVICES ADMINISTRATION & MANAGEMENT; STATISTICS & RESEARCH METHODS