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In order to meet regulatory approval, pharmaceutical companies often mustdemonstrate that new vaccines reduce the total risk of a post-infection outcomelike transmission, symptomatic disease, severe illness, or death in randomized,placebo-controlled trials. Given that infection is a necessary precondition fora post-infection outcome, one can use principal stratification to partition thetotal causal effect of vaccination into two causal effects: vaccine efficacyagainst infection, and the principal effect of vaccine efficacy against apost-infection outcome in the patients that would be infected under bothplacebo and vaccination. Despite the importance of such principal effects topolicymakers, these estimands are generally unidentifiable, even under strongassumptions that are rarely satisfied in real-world trials. We develop a novelmethod to nonparametrically point identify these principal effects whileeliminating the monotonicity assumption and allowing for measurement error.Furthermore, our results allow for multiple treatments, and are general enoughto be applicable outside of vaccine efficacy. Our method relies on the factthat many vaccine trials are run at geographically disparate health centers,and measure biologically-relevant categorical pretreatment covariates. We showthat our method can be applied to a variety of clinical trial settings wherevaccine efficacy against infection and a post-infection outcome can be jointlyinferred. This can yield new insights from existing vaccine efficacy trial dataand will aid researchers in designing new multi-arm clinical trials.

Identified vaccine efficacy for binary post-infection outcomes under misclassification without monotonicity