How should we select test-negative controls? A causal perspective in the era of multiplex respiratory testing

2026-07-30

AI Summary

In the context of multiplex PCR testing for respiratory pathogens, the lack of systematic guidance in selecting test-negative controls compromises the accuracy of vaccine effectiveness estimates. This study addresses this gap by applying causal inference theory to the test-negative design under multiplex testing for the first time. It introduces a novel classification of controls—distinguishing between exposure proxies and healthcare-seeking proxies—and proposes three practical selection principles. Furthermore, the authors develop a pathogen-specific screening estimator that remains unbiased under both co-detection and all-negative scenarios. Simulation and empirical analyses demonstrate that conventional pooled estimators are vulnerable to bias induced by individual control pathogens, whereas the proposed approach maintains robustness and improves estimation accuracy across diverse testing outcomes.

Abstract

The test-negative design (TND) is widely used to estimate vaccine effectiveness (VE) for respiratory pathogens by comparing vaccination odds among test-positive cases versus test-negative controls. A central yet underexplored design element is which test-negative illnesses constitute valid controls. With rapid multiplex PCR panels, investigators can now identify specific non-focal pathogens among test-negative patients, allowing for better characterization of ``test-negative illness'', but also revealing a mixture of control outcomes that may each satisfy or violate causal assumptions. We synthesize recent causal identification results for the TND and show that they imply two distinct interpretations of control selection: 1) a sampling view in which controls represent the source population and 2) a bias-correction view in which controls function as negative control outcomes under equi-confounding. Building on these interpretations, we develop a framework for multiplex-informed control selection. We propose a taxonomy that distinguishes controls that serve primarily as exposure proxies (sharing unmeasured determinants of infection) from those that serve as testing proxies (sharing unmeasured determinants of care-seeking), derive implications for pathogen-specific and pooled estimators, and suggest three practical principles for control selection: vaccine irrelevance, avoidance of entanglement with other interventions, and testing-process comparability. We also formalize nuances introduced by multiplex panels, including co-detections and pan-negative episodes, and outline when standard pooled estimators remain valid versus when alternative estimators are needed. In simulations across 9 scenarios, we demonstrate violations concentrated in a single control pathogen can substantially bias pooled TND estimates, whereas a pre-specified pathogen screening estimator remained unbiased.

Problem

• test-negative design
• vaccine effectiveness
• causal inference
• multiplex testing
• control selection

Innovation

• test-negative design
• causal inference
• multiplex PCR
• control selection
• vaccine effectiveness