  Developing accurate and reliable methods to estimate vaccine protection is a
key goal in immunology and public health. While several statistical methods
have been proposed, their potential inaccuracy in capturing fast intra-seasonal
waning of vaccine-induced protection needs to be rigorously investigated. To
compare statistical methods for vaccine effectiveness (VE) estimation, we
generated simulated data using a multiscale agent-based model of an epidemic
with an acute viral infection and differing extents of VE waning. We extended
the previously proposed framework for VE measures based on the observational
data richness to assess changes of vaccine-induced protection with time. While
VE measures based on hard-to-collect information (e.g. exact timing of
exposures) were accurate, usually VE studies rely on time-to-infection data and
the Cox proportional hazard model. We found that its extension utilizing scaled
Schoenfeld residuals, previously proposed for capturing VE waning, was
unreliable in capturing both the degree of waning and its functional form and
identified the mathematical factors contributing to this unreliability. We
showed that partitioning time and including a time-vaccine interaction term in
the Cox model significantly improved estimation of VE waning, even in the case
of dramatic, rapid waning. We also proposed how to optimize the partitioning
scheme. Using simulated data, we compared different measures of VE for
capturing the intra-seasonal waning of vaccine-induced protection. We propose
an extension of the Cox model based on including a time-vaccine interaction
term with further optimization of partitioning time. These findings may guide
future analysis of VE waning in observational data.
