AI 中文总结
该研究针对多重呼吸道检测时代的检测阴性设计，提出基于因果视角的对照选择框架，通过分类对照类型、明确原则及模拟验证，解决对照选择偏倚问题以准确评估疫苗有效性。
AI 中文摘要
检测阴性设计（TND）被广泛用于评估呼吸道病原体的疫苗有效性（VE），其核心是比较检测阳性病例与检测阴性对照中的疫苗接种率。一个尚未被充分探讨的关键设计要素是：哪些检测阴性疾病可作为有效对照。随着多重PCR检测面板的快速发展，研究人员现在能够在检测阴性患者中识别出特定的非靶标病原体，这有助于更准确地界定“检测阴性疾病”，但也显示出对照结果存在混合情况，每种情况可能满足或违反因果假设。我们综合了TND的最新因果识别结果，表明这些结果隐含两种对照选择的不同解读：1）抽样视角，即对照代表源人群；2）偏倚校正视角，即对照在等混杂下作为阴性对照结局。基于这些解读，我们开发了一种基于多重检测的对照选择框架。我们提出一种分类法，将主要作为暴露代理（共享感染的未测量决定因素）的对照与作为检测代理（共享就医行为的未测量决定因素）的对照区分开来，推导了病原体特异性和合并估计量的含义，并提出三项对照选择的实用原则：疫苗无关性、避免与其他干预措施纠缠、检测过程可比性。我们还明确了多重检测面板带来的细微差别，包括共检测和全阴性事件，并概述了标准合并估计量何时仍有效、何时需要替代估计量。在9种场景的模拟中，我们证明集中在单一对照病原体的违反情况会严重偏倚合并TND估计量，而预先指定的病原体筛选估计量则保持无偏。
英文摘要
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.
