Nonparametric bounds for vaccine effects in randomized trials

TL;DR

This paper develops nonparametric bounds for vaccine efficacy in randomized trials where blinding may be broken, accounting for behavioral effects and unmeasured confounding, using linear programming and monotonicity methods.

Contribution

It relaxes strong assumptions in existing bounds, providing new nonparametric bounds under various causal structures for vaccine effects.

Findings

Bounds vary across different causal scenarios.

Bounds are demonstrated with synthetic and semi-synthetic COVID-19 data.

Method accommodates unmeasured confounding and behavioral effects.

Abstract

Vaccine randomized trials are typically designed to be blinded, ensuring that the estimated vaccine efficacy (VE) reflects the immunological effect of the vaccine. When blinding is broken, however, the estimated VE reflects not only the immunological effect but also behavioral effects stemming from participants' awareness of their treatment status. Recent work has proposed alternative causal estimands to the standard VE to address this issue, but their point identification results require a strong assumption: the absence of unmeasured common causes of infection risk and participants' belief about whether they received the vaccine. Personality traits, for example, may plausibly violate this assumption. We relax this assumption and derive nonparametric causal bounds for different types of VE. We construct these bounds using two approaches: linear programming-based and monotonicity-based methods. We further consider several possible causal structures for vaccine trials and show how the nonparametric bounds differ across these scenarios. Finally, we illustrate the performance of the proposed bounds using fully synthetic data and a semi-synthetic data example based on a COVID-19 vaccine trial.