Sensitivity Analysis for the Test-Negative Design

TL;DR

This paper analyzes the test-negative design used in vaccine effectiveness studies, proposing sensitivity analysis methods to account for unmeasured confounding and applying them to COVID-19 vaccine data.

Contribution

It introduces new sensitivity analysis techniques for the test-negative design, addressing unmeasured confounding and improving causal effect estimation.

Findings

Proposed methods provide narrower bounds on vaccine effectiveness estimates.

Applied methods to COVID-19 vaccine data demonstrating practical utility.

Compared different assumptions for causal identification in test-negative studies.

Abstract

The test-negative design has become popular for evaluating the effectiveness of post-licensure vaccines using observational data. In addition to its logistical convenience on data collection, the design is also believed to control for the differential health-care-seeking behavior between vaccinated and unvaccinated individuals, an important while often unmeasured confounder between the vaccination and infection. Hence, the design has been employed routinely to monitor seasonal flu vaccines and more recently to measure the COVID-19 vaccine effectiveness. Despite its popularity, the design has been questioned, in particular about its ability to fully control for the unmeasured confounding. In this paper, we explore deviations from a perfect test-negative design, and propose various sensitivity analysis methods for estimating the effect of vaccination measured by the causal odds ratio on the subpopulation of individuals with good health-care-seeking behavior. We start with point identification of the causal odds ratio under a test-negative design, comparing different forms of identification assumptions and their corresponding estimands. We then propose two approaches for conducting sensitivity analysis, addressing the influence of the unmeasured confounding in two different ways. Specifically, one approach investigates partial control for unmeasured confounding in the test-negative design, while the other examines the impact of unmeasured confounding on both vaccination and infection. Furthermore, we combine these approaches to provide narrower bounds on the true causal odds ratio, and further sharpen the bounds by restricting the treatment effect heterogeneity. Finally, we apply the proposed methods to evaluate the effectiveness of COVID-19 vaccines using observational data from test-negative designs.