Exposure-Induced Confounding of Missingness in Cause of Failure with Applications in Estimating Strain-Specific Efficacy of Vaccines

• Activity Number: 37 - Novel Semiparametric Methods for Causal Inference
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
• Sponsor: SSC (Statistical Society of Canada)
• Author(s): David Benkeser* and Lars van der Laan and Ziyue Wu
• Companies: Emory University, Rollins School of Public Health and University of Washington and Emory University, Rollins School of Public Health
• Keywords: causal inference; missing data; semiparametric efficiency; targeted minimum loss estimation; vaccines; COVID-19

Abstract

A common goal of studies of preventive vaccines is to estimate whether and how their efficacy for preventing infection and/or disease varies by the strain of the infecting pathogen. This issue has come to the forefront of public interest over the past year as new strains of SARS-CoV2 have emerged. One of the challenges in learning about strain-specific efficacy of vaccines from randomized trials is the fact that many breakthrough infections have missing sequence data. This missingness can bias estimation of strain-specific efficacy. For example, vaccines often cause less virulent forms of infection, leading to lower levels of viral genetic material in samples taken from breakthrough infections and in turn, a higher probability of sequencing failure. On the other hand, some strains of pathogen may be naturally less virulent irrespective of host vaccination status. Thus, viral load may be an exposure-induced confounder in the context of estimation of strain-specific efficacy. In this talk, we will describe methodology to address this bias using flexible semiparametric methods for causal inference and illustrate these methods using examples from modern vaccine studies.