Targeted maximum likelihood estimation of vaccine effectiveness and immune correlates in test-negative design studies with missing data

2026-05-20

AI Summary

This study addresses bias in causal inference within test-negative design (TND) studies arising from missing exposure data and residual confounding. It introduces targeted maximum likelihood estimation (TMLE) into the TND framework for the first time, integrating a semiparametric logistic regression model to enable flexible, data-adaptive confounder adjustment under the missing-at-random (MAR) assumption. The proposed method accommodates two-phase sampling designs commonly used in immunological analyses and possesses desirable statistical properties, including efficiency and asymptotic linearity. Its finite-sample performance is evaluated through plasmode simulations, and it is successfully applied to a cohort derived from the Moderna Phase III COVID-19 vaccine trial to yield accurate estimates of vaccine effectiveness and the immune correlates of protection based on antibody markers.

Abstract

The test-negative design (TND) is a resource-efficient observational study design that can assess vaccine effectiveness and exposure-proximal immune correlates of disease. The TND enrolls symptomatic individuals seeking diagnostic testing and compares case status by an exposure variable, such as vaccination status or immune marker level, that is measured at testing. While the TND reduces confounding by healthcare-seeking behavior, other sources of confounding may remain. TND studies may also have missing data in the exposure variable due to incomplete records or two-phase sampling designs. We present a targeted maximum likelihood estimation approach involving a semiparametric logistic regression model that targets a causal conditional risk ratio of symptomatic disease in the healthcare-seeking population. Under causal and missing at random assumptions, our method produces an efficient, asymptotically linear estimator that provides flexible, data-driven confounding control and valid causal inference when analyzing TND studies with missing exposure variable data. We evaluate our method's finite sample properties using plasmode simulations of a two-phase TND immune correlates study. We also apply our method to assess COVID-19 vaccine effectiveness and antibody marker correlates of COVID-19 from TND study cohorts derived from the Moderna Coronavirus Efficacy phase 3 trial.

Problem

• test-negative design
• vaccine effectiveness
• immune correlates
• missing data
• confounding

Innovation

• targeted maximum likelihood estimation
• test-negative design
• missing data
• causal inference
• immune correlates