Infectious DiseasesTheoretical Framework for Retrospective Studies of the Effectiveness of SARS-CoV-2 VaccinesLewnard, Joseph A.a,b,c; Patel, Manish M.d; Jewell, Nicholas P.e,f; Verani, Jennifer R.d; Kobayashi, Miwakod; Tenforde, Mark W.d; Dean, Natalie E.g; Cowling, Benjamin J.h; Lopman, Benjamin A.i Author Information From the aDivision of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA bDivision of Infectious Diseases & Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, CA cCenter for Computational Biology, College of Engineering, University of California, Berkeley, Berkeley, CA dCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA eDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom fDivision of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA gDepartment of Biostatistics, University of Florida, Gainesville, FL hWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, University of Hong Kong, Hong Kong, China iDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Submitted January 21, 2021; accepted April 1, 2021 This work was supported by grants R01-AI14812701 from the National Institute for Allergy and Infectious Diseases to N.P.J. and J.A.L., and R01-AI139761 from the National Institute for Allergy and Infectious Diseases to N.E.D. Code for replication is available from the corresponding author via https://github.com/joelewnard/covidTND. J.A.L. has received grants and consulting fees from Pfizer, Inc., unrelated to this research. The remaining authors report no conflicts of interest. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Correspondence: Joseph A. Lewnard, 2121 Berkeley Way, Room 5410, Berkeley, CA 94720. E-mail: [email protected]. Epidemiology 32(4):p 508-517, July 2021. | DOI: 10.1097/EDE.0000000000001366 Buy SDC Metrics Abstract Observational studies of the effectiveness of vaccines to prevent COVID-19 are needed to inform real-world use. Such studies are now underway amid the ongoing rollout of SARS-CoV-2 vaccines globally. Although traditional case-control and test-negative design studies feature prominently among strategies used to assess vaccine effectiveness, such studies may encounter important threats to validity. Here, we review the theoretical basis for estimation of vaccine direct effects under traditional case-control and test-negative design frameworks, addressing specific natural history parameters of SARS-CoV-2 infection and COVID-19 relevant to these designs. Bias may be introduced by misclassification of cases and controls, particularly when clinical case criteria include common, nonspecific indicators of COVID-19. When using diagnostic assays with high analytical sensitivity for SARS-CoV-2 detection, individuals testing positive may be counted as cases even if their symptoms are due to other causes. The traditional case-control design may be particularly prone to confounding due to associations of vaccination with healthcare-seeking behavior or risk of infection. The test-negative design reduces but may not eliminate this confounding, for instance, if individuals who receive vaccination seek care or testing for less-severe illness. These circumstances indicate the two study designs cannot be applied naively to datasets gathered through public health surveillance or administrative sources. We suggest practical strategies to reduce bias in vaccine effectiveness estimates at the study design and analysis stages. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.