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Estimating the probability of demonstrating vaccine efficacy in the declining Ebola epidemic: a Bayesian modelling approach
  1. Anton Camacho,
  2. Rosalind M Eggo,
  3. Sebastian Funk,
  4. Conall H Watson,
  5. Adam J Kucharski,
  6. W John Edmunds
    1. Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
    1. Correspondence to Dr Rosalind M Eggo; r.eggo{at}lshtm.ac.uk

    Abstract

    Objectives 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.

    • Clinical trials < THERAPEUTICS
    • STATISTICS & RESEARCH METHODS
    • Protocols & guidelines < HEALTH SERVICES ADMINISTRATION & MANAGEMENT

    This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

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