Real-world vaccine effectiveness has increasingly been studied using
matching-based approaches, particularly in observational cohort studies
following the target trial emulation framework. Although matching is appealing
in its simplicity, it suffers important limitations in terms of clarity of the
target estimand and the efficiency or precision with which is it estimated.
Scientifically justified causal estimands of vaccine effectiveness may be
difficult to define owing to the fact that vaccine uptake varies over calendar
time when infection dynamics may also be rapidly changing. We propose a causal
estimand of vaccine effectiveness that summarizes vaccine effectiveness over
calendar time, similar to how vaccine efficacy is summarized in a randomized
controlled trial. We describe the identification of our estimand, including its
underlying assumptions, and propose simple-to-implement estimators based on two
hazard regression models. We apply our proposed estimator in simulations and in
a study to assess the effectiveness of the Pfizer-BioNTech COVID-19 vaccine to
prevent infections with SARS-CoV2 in children 5-11 years old. In both settings,
we find that our proposed estimator yields similar scientific inferences while
providing significant efficiency gains over commonly used matching-based
estimators.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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