IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-29534-8.html
   My bibliography  Save this article

Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

Author

Listed:
  • Simon Arsène

    (Novadiscovery SA)

  • Claire Couty

    (Novadiscovery SA)

  • Igor Faddeenkov

    (Novadiscovery SA)

  • Natacha Go

    (Novadiscovery SA)

  • Solène Granjeon-Noriot

    (Novadiscovery SA)

  • Daniel Šmít

    (Novadiscovery SA)

  • Riad Kahoul

    (Novadiscovery SA)

  • Ben Illigens

    (Novadiscovery SA
    Dresden International University)

  • Jean-Pierre Boissel

    (Novadiscovery SA)

  • Aude Chevalier

    (OM Pharma)

  • Lorenz Lehr

    (OM Pharma)

  • Christian Pasquali

    (OM Pharma)

  • Alexander Kulesza

    (Novadiscovery SA)

Abstract

Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.

Suggested Citation

  • Simon Arsène & Claire Couty & Igor Faddeenkov & Natacha Go & Solène Granjeon-Noriot & Daniel Šmít & Riad Kahoul & Ben Illigens & Jean-Pierre Boissel & Aude Chevalier & Lorenz Lehr & Christian Pasquali, 2022. "Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29534-8
    DOI: 10.1038/s41467-022-29534-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-29534-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-29534-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nicola Jones, 2020. "How COVID-19 is changing the cold and flu season," Nature, Nature, vol. 588(7838), pages 388-390, December.
    2. Aban, Inmaculada B. & Cutter, Gary R. & Mavinga, Nsoki, 2009. "Inferences and power analysis concerning two negative binomial distributions with an application to MRI lesion counts data," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 820-833, January.
    3. Heidi Ledford, 2021. "The COVID pandemic’s lingering impact on clinical trials," Nature, Nature, vol. 595(7867), pages 341-342, July.
    4. David Adam, 2020. "Special report: The simulations driving the world’s response to COVID-19," Nature, Nature, vol. 580(7803), pages 316-318, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Islam, Asad & Pakrashi, Debayan & Vlassopoulos, Michael & Wang, Liang Choon, 2021. "Stigma and misconceptions in the time of the COVID-19 pandemic: A field experiment in India," Social Science & Medicine, Elsevier, vol. 278(C).
    2. Rosa Esteve & Alicia Eva López-Martínez & Gema Teresa Ruíz-Párraga & Elena Rocío Serrano-Ibáñez & Carmen Ramírez-Maestre, 2020. "Pain Acceptance and Pain-Related Disability Predict Healthcare Utilization and Medication Intake in Patients with Non-Specific Chronic Spinal Pain," IJERPH, MDPI, vol. 17(15), pages 1-12, July.
    3. Baíllo, A. & Berrendero, J.R. & Cárcamo, J., 2009. "Tests for zero-inflation and overdispersion: A new approach based on the stochastic convex order," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2628-2639, May.
    4. Alejandro Danón & Andrés S. Mena & Andrés Ramasco, 2021. "COVID-19 a nivel local: SEIR+ un modelo para proyectar escenarios epidemiológicos y demandas hacia el sistema sanitario," Ensayos de Política Económica, Departamento de Investigación Francisco Valsecchi, Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina., vol. 3(3), pages 1-24, Octubre.
    5. Jones, Mark & Dobson, Annette & Onslow, Mark & Carey, Brenda, 2009. "Negative binomial mixed models for analysis of stuttering rates," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4590-4600, October.
    6. Pablo Boullosa & Adrián Garea & Iván Area & Juan J. Nieto & Jorge Mira, 2022. "Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-15, July.
    7. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29534-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.