Assessing Durability of Vaccine Effect Following Blinded Crossover in COVID-19 Vaccine Efficacy Trials.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
14 Dec 2020
Historique:
entrez: 18 12 2020
pubmed: 19 12 2020
medline: 19 12 2020
Statut: epublish

Résumé

Several candidate vaccines to prevent COVID-19 disease have entered large-scale phase 3 placebo-controlled randomized clinical trials and some have demonstrated substantial short-term efficacy. Efficacious vaccines should, at some point, be offered to placebo participants, which will occur before long-term efficacy and safety are known. Following vaccination of the placebo group, we show that placebo-controlled vaccine efficacy can be derived by assuming the benefit of vaccination over time has the same profile for the original vaccine recipients and the placebo crossovers. This reconstruction allows estimation of both vaccine durability and potential vaccine-associated enhanced disease. Post-crossover estimates of vaccine efficacy can provide insights about durability, identify waning efficacy, and identify late enhancement of disease, but are less reliable estimates than those obtained by a standard trial where the placebo cohort is maintained. As vaccine efficacy estimates for post-crossover periods depend on prior vaccine efficacy estimates, longer pre-crossover periods with higher case counts provide better estimates of late vaccine efficacy. Further, open-label crossover may lead to riskier behavior in the immediate crossover period for the unblinded vaccine arm, confounding vaccine efficacy estimates for all post-crossover periods. We advocate blinded crossover and continued follow-up of trial participants to best assess vaccine durability and potential delayed enhancement of disease. This approach allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain participants on placebo, yet still allows important insights about immunological and clinical effectiveness over time.

Sections du résumé

BACKGROUND BACKGROUND
Several candidate vaccines to prevent COVID-19 disease have entered large-scale phase 3 placebo-controlled randomized clinical trials and some have demonstrated substantial short-term efficacy. Efficacious vaccines should, at some point, be offered to placebo participants, which will occur before long-term efficacy and safety are known.
METHODS METHODS
Following vaccination of the placebo group, we show that placebo-controlled vaccine efficacy can be derived by assuming the benefit of vaccination over time has the same profile for the original vaccine recipients and the placebo crossovers. This reconstruction allows estimation of both vaccine durability and potential vaccine-associated enhanced disease.
RESULTS RESULTS
Post-crossover estimates of vaccine efficacy can provide insights about durability, identify waning efficacy, and identify late enhancement of disease, but are less reliable estimates than those obtained by a standard trial where the placebo cohort is maintained. As vaccine efficacy estimates for post-crossover periods depend on prior vaccine efficacy estimates, longer pre-crossover periods with higher case counts provide better estimates of late vaccine efficacy. Further, open-label crossover may lead to riskier behavior in the immediate crossover period for the unblinded vaccine arm, confounding vaccine efficacy estimates for all post-crossover periods.
CONCLUSIONS CONCLUSIONS
We advocate blinded crossover and continued follow-up of trial participants to best assess vaccine durability and potential delayed enhancement of disease. This approach allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain participants on placebo, yet still allows important insights about immunological and clinical effectiveness over time.

Identifiants

pubmed: 33336213
doi: 10.1101/2020.12.14.20248137
pmc: PMC7745130
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIAID NIH HHS
ID : R37 AI029168
Pays : United States
Organisme : NIAID NIH HHS
ID : UM1 AI068614
Pays : United States
Organisme : NIAID NIH HHS
ID : R37 AI054165
Pays : United States
Organisme : NIAID NIH HHS
ID : UM1 AI068635
Pays : United States
Organisme : NIAID NIH HHS
ID : UM1 AI068617
Pays : United States

Commentaires et corrections

Type : UpdateIn

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Auteurs

Dean Follmann (D)

Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.

Jonathan Fintzi (J)

Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.

Michael P Fay (MP)

Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.

Holly E Janes (HE)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Lindsey Baden (L)

Brigham and Women's Hospital, Boston, MA, USA.

Hana El Sahly (HE)

Baylor College of Medicine, Houston, TX, USA.

Thomas R Fleming (TR)

Department of Biostatistics, University of Washington, Seattle, WA, USA.

Devan V Mehrotra (DV)

Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA, USA.

Lindsay N Carpp (LN)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Michal Juraska (M)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

David Benkeser (D)

Rollins School of Public Health, Emory University, Atlanta, GA, USA.

Deborah Donnell (D)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Youyi Fong (Y)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Shu Han (S)

Moderna, Inc., Cambridge, MA, USA.

Ian Hirsch (I)

Biometrics, Late-stage Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.

Ying Huang (Y)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Yunda Huang (Y)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Ollivier Hyrien (O)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Alex Luedtke (A)

Department of Statistics, University of Washington, Seattle, WA, USA.

Marco Carone (M)

Department of Biostatistics, University of Washington, Seattle, WA, USA.

Martha Nason (M)

Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.

An Vandebosch (A)

Janssen R&D, Janssen Pharmaceuticals NV, Beerse, Belgium.

Honghong Zhou (H)

Moderna, Inc., Cambridge, MA, USA.

Iksung Cho (I)

Biostatistics, Novavax, Inc., Gaithersburg, MD, USA.

Erin Gabriel (E)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

James G Kublin (JG)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Myron S Cohen (MS)

Institute for Global Health and Infectious Diseases, University of North Carolina, Chapel Hill, NC, USA.

Lawrence Corey (L)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Peter B Gilbert (PB)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Kathleen M Neuzil (KM)

University of Maryland School of Medicine, Baltimore, MD, USA.

Classifications MeSH