  Observational data are often used to estimate real-world effectiveness and
durability of coronavirus disease 2019 (COVID-19) vaccines. A sequence of
nested trials can be emulated to draw inference from such data while minimizing
selection bias, immortal time bias, and confounding. Typically, when nested
trial emulation (NTE) is employed, effect estimates are pooled across trials to
increase statistical efficiency. However, such pooled estimates may lack a
clear interpretation when the treatment effect is heterogeneous across trials.
In the context of COVID-19, vaccine effectiveness quite plausibly will vary
over calendar time due to newly emerging variants of the virus. This manuscript
considers a NTE inverse probability weighted estimator of vaccine effectiveness
that may vary over calendar time, time since vaccination, or both. Statistical
testing of the trial effect homogeneity assumption is considered. Simulation
studies are presented examining the finite-sample performance of these methods
under a variety of scenarios. The methods are used to estimate vaccine
effectiveness against COVID-19 outcomes using observational data on over
120,000 residents of Abruzzo, Italy during 2021.
