Conference Program Home
  My Program

All Times EDT

Abstract Details

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)
Abstract #320386
Title: Exposure-Induced Confounding of Missingness in Cause of Failure with Applications in Estimating Strain-Specific Efficacy of Vaccines
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

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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program