Statistical Methods for Assessing COVID-19 Vaccine Effectiveness and Immune Correlates in Test-Negative Design Studies

Abstract

Vaccines have been essential for protecting against COVID-19 and must be continually evaluated and updated in post-marketing settings. Understanding the relationship between COVID-19 and immune markers correlated with vaccination or infection can also inform vaccine development and updates. The test-negative design (TND) is a resource-efficient observational study design that enrolls symptomatic individuals who obtain SARS-CoV-2 testing and compares vaccination status or immune marker measures between cases who test positive and noncases who test negative. While the TND reduces bias from healthcare-seeing behavior, additional biases from confounding, missing data, and selection mechanisms may persist. This dissertation proposes to improve existing TND analysis methods to obtain more robust and interpretable causal estimates of COVID-19 vaccine effectiveness and protective immune correlate levels. The first project extends a targeted maximum likelihood estimator under a partially linear logistic regression model to a TND setting. This semiparametric logistic regression method targets a causal conditional risk ratio of COVID-19 in the healthcare-seeking population and allows for flexible, data-driven confounding adjustment and missing data in the exposure variable. The second project investigates conditions that allow the TND to obtain unbiased and precise COVID-19 vaccine effectiveness estimates. This work reanalyzes five phase 3 COVID-19 Prevention Network vaccine efficacy trials as TND studies and evaluates if COVID-19 vaccines affect other causes of COVID-19-like symptoms. The third project extends a negative control method that addresses unmeasured confounding and selection bias in TND studies. This extension targets the causal risk ratios of viral genotype-specific symptomatic COVID-19 and incorporates inverse probability weighting and augmented inverse probability weighting to account for COVID-19 cases with missing viral genotypes.