Vaccine Efficacy Estimands Implied by Common Estimators Used in Individual Randomized Field Trials

TL;DR

This paper analyzes how common estimators in vaccine trials imply different vaccine efficacy estimands, discussing their interpretation, approximation quality, and potential biases over time.

Contribution

It provides a nonparametric formulation of VE estimands, compares their approximations, and discusses interpretational challenges and sensitivity analyses in vaccine efficacy studies.

Findings

Ratio effects are approximately equal at low event rates.

Full immunization estimands exclude early events before vaccine ramp-up.

Local VE estimands face interpretational difficulties like depletion of susceptibles bias.

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.