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Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningComputer Vision: Adversarial Attacks & Robustness
Vaccine Efficacy Estimands Implied by Common Estimators Used in Individual Randomized Field Trials
🤖 AI Summary
This study addresses the causal interpretation challenges in estimating vaccine efficacy (VE) against susceptibility in individually randomized field trials when natural exposure is unobserved. The authors develop a nonparametric framework to systematically analyze time-to-event–based VE estimands, distinguishing between full-course effects and local instantaneous effects, and clarifying their causal meanings and identifying assumptions. Through frailty modeling and sensitivity analyses, they evaluate the performance of common effect measures—including incidence rate ratios, cumulative incidence ratios, hazard ratios, and odds ratios—under low event rates, revealing that local VE estimates are susceptible to depletion-of-susceptibles bias. The work delineates the implicit assumptions and validity conditions underlying widely used VE estimators, thereby providing a more rigorous statistical foundation for causal inference on vaccine efficacy.
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📝 Abstract
We review vaccine efficacy (VE) estimands for susceptibility in individual randomized trials with natural (unmeasured) exposure, where individual responses are measured as time from vaccination until an event (e.g., disease from the infectious agent). Common VE estimands are written as $1-\theta$, where $\theta$ is some ratio effect measure (e.g., ratio of incidence rates, cumulative incidences, hazards, or odds) comparing outcomes under vaccination versus control. Although the ratio effects are approximately equal with low control event rates, we explore the quality of that approximation using a nonparametric formulation. Traditionally, the primary endpoint VE estimands are full immunization (or biological) estimands that represent a subset of the intent-to-treat population, excluding those that have the event before the vaccine has been able to ramp-up to its full effect, requiring care for proper causal interpretation. Besides these primary VE estimands that summarize an effect of the vaccine over the full course of the study, we also consider local VE estimands that measure the effect at particular time points. We discuss interpretational difficulties of local VE estimands (e.g., depletion of susceptibles bias), and using frailty models as sensitivity analyses for the individual-level causal effects over time.
Problem
Research questions and friction points this paper is trying to address.
vaccine efficacy
estimands
susceptibility
randomized trials
causal interpretation
Innovation
Methods, ideas, or system contributions that make the work stand out.
vaccine efficacy estimands
nonparametric formulation
depletion of susceptibles
frailty models
causal interpretation
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