Vaccine efficacy against naturally asymptomatic infections: A novel estimand for quantifying vaccine effects

Abstract The naive approach to estimating the effects of a vaccine on asymptomatic infections, which compares the risk of asymptomatic infection among vaccinated and unvaccinated individuals, can be misleading because it is comprised of two effects: the vaccine preventing asymptomatic infections and the vaccine converting symptomatic to asymptomatic infections. When the latter effect is strong, vaccines can appear harmful with respect to asymptomatic infections. Using a causal principal stratification framework, we formalize an estimand, vaccine efficacy against naturally asymptomatic infection (VENAI), that describes the effectiveness of a vaccine in preventing asymptomatic infections among individuals who would naturally (i.e., in the absence of vaccine) be expected to be asymptomatic. This estimand excludes vaccine effects that convert symptomatic cases to asymptomatic infections, and we demonstrate how this makes it a more natural analogue of the usual vaccine efficacy estimands against infection and symptomatic disease. We describe the assumptions under which this estimand can be identified and estimated from randomized and observational studies. We further identify and estimate bounds that do not require cross-world independence assumptions and characterize sensitivity analyses around the main assumption needed for identification. Finally, we apply these methods to a randomized trial of the COVID-19 mRNA-1273 vaccine. In this trial, VENAI was higher than standard estimates of efficacy against asymptomatic infections and was similar in magnitude to efficacy against any infection. Reporting VENAI in vaccine trials in addition to other vaccine effects would improve interpretability, could broaden understanding of vaccine impact on transmission, and provide insights into immunological mechanisms. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was made possible by cooperative agreement CDC-RFA-FT-23-0069 (Main Award #0000075279, Supplement Award # 0000081613) from the CDC's Center for Forecasting and Outbreak Analytics. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval was waived by the Emory IRB. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Data from the COVE study are available on reasonable request to the corresponding author. Data from the PROVIDE study are publicly available at clinepidb.org. Computing code for the analysis is available at http://github.com/allicodi/ve_nai and for a corresponding R package at https://github.com/allicodi/VEnai.