Primary Submission Category: Bayesian Causal Inference
Identified vaccine efficacy for binary post-infection outcomes under misclassification without monotonicity
Authors: Rob Trangucci, Yang Chen, Jon Zelner,
Presenting Author: Rob Trangucci*
In order to meet regulatory approval, pharmaceutical companies often must demonstrate that new vaccines reduce the total risk of a post-infection outcome like transmission, symptomatic disease, or severe illness in randomized, placebo-controlled trials. Given that infection is necessary for a post-infection outcome, one can use principal stratification to partition the total causal effect of vaccination into two causal effects: vaccine efficacy against infection, and the principal effect of vaccine efficacy against a post-infection outcome in always-infected patients. Despite the importance of such principal effects to policymakers, these estimands are generally unidentifiable, even under strong assumptions that are rarely satisfied in real-world trials. We develop a novel method to nonparametrically point identify these principal effects while eliminating the monotonicity assumption and allowing for measurement error. Moreover, our results allow for multiple treatments, and are general enough to be applicable outside of vaccine efficacy. Our method relies on the fact that many vaccine trials are multi-center trials, and measure biologically-relevant categorical pretreatment covariates. We show our method can be applied to clinical trial settings where vaccine efficacy against infection and a post-infection outcome can be jointly inferred. This can yield new insights from existing vaccine efficacy trial data and will aid researchers in designing new multi-arm clinical trials.