Debiasing Hazard-Based, Time-Varying Vaccine Effects Using Vaccine-Irrelevant Infections: An Observational Extension of a Phase 3 COVID-19 Vaccine Efficacy Trial

Thomas M. Menino Convention & Exhibition Center 

Understanding how vaccine effectiveness (VE) changes over time can provide evidence-based guidance
for public health decision making. While commonly reported by practitioners, time-varying VE estimates obtained
using Cox regression are vulnerable to hidden biases. To address these limitations, we describe how to leverage
vaccine-irrelevant infections to identify hazard-based, time-varying VE in the presence of unmeasured confounding
and selection bias. We articulate assumptions under which our approach identifies a causal effect of an intervention
deferring vaccination and interaction with the community in which infections circulate. We develop sieve and efficient influence curve-based estimators and discuss imposing monotone shape constraints and estimating VE against multiple variants. As a case study, we examine the observational booster phase of the Coronavirus Vaccine Efficacy (COVE) trial of the Moderna mRNA-1273 COVID-19 vaccine which used symptom-triggered multiplex PCR testing to identify acute respiratory illnesses (ARIs) caused by SARS-CoV-2 and 20 off-target pathogens previously identified as compelling negative controls for COVID-19. Accounting for vaccine-irrelevant ARIs supported that the mRNA-1273 booster was more effective and durable against Omicron COVID-19 than suggested by Cox regression. Our work offers an approach to mitigate bias in hazard-based, time-varying treatment effects in randomized and non-randomized studies using negative controls.

Negative controls

COVID-19 vaccine

Time-varying treatment effects selection bias

Unmeasured confounding

Hazard function