Background: Diagnostic test accuracy (DTA) studies, like etiological studies, are susceptible to various biases including reference standard error bias, partial verification bias, spectrum effect, confounding, and bias from misassumption of conditional independence. While directed acyclic graphs (DAGs) are widely used in etiological research to identify and illustrate bias structures, they have not been systematically applied to DTA studies. Methods: We developed DAGs to illustrate the causal structures underlying common biases in DTA studies. For each bias, we present the corresponding DAG structure and demonstrate the parallel with equivalent biases in etiological studies. We use real-world examples to illustrate each bias mechanism. Results: We demonstrate that five major biases in DTA studies can be represented using DAGs with clear structural parallels to etiological studies: reference standard error bias corresponds to exposure misclassification, misassumption of conditional independence creates spurious correlations similar to unmeasured confounding, spectrum effect parallels effect modification, confounding operates through backdoor paths in both settings, and partial verification bias mirrors selection bias. These DAG representations reveal the causal mechanisms underlying each bias and suggest appropriate correction strategies. Conclusions: DAGs provide a valuable framework for understanding bias structures in DTA studies and should complement existing quality assessment tools like STARD and QUADAS-2. We recommend incorporating DAGs during study design to prospectively identify potential biases and during reporting to enhance transparency. DAG construction requires interdisciplinary collaboration and sensitivity analyses under alternative causal structures.

翻译：背景：与病因学研究类似，诊断准确性研究易受多种偏倚影响，包括参考标准误差偏倚、部分证实偏倚、谱效应、混杂以及条件独立性误设导致的偏倚。虽然有向无环图在病因学研究中已被广泛用于识别和阐释偏倚结构，但其尚未系统应用于诊断准确性研究。方法：我们构建了有向无环图以阐释诊断准确性研究中常见偏倚的因果结构。针对每种偏倚，我们呈现了相应的有向无环图结构，并展示了其与病因学研究中等效偏倚的对应关系。我们通过真实案例说明每种偏倚机制。结果：我们证明诊断准确性研究中的五大类偏倚均可通过有向无环图表示，且与病因学研究具有明确的结构对应性：参考标准误差偏倚对应暴露错误分类，条件独立性误设产生类似于未测量混杂的伪相关，谱效应平行于效应修饰，混杂通过后门路径在两种情境中发挥作用，部分证实偏倚则与选择偏倚相对应。这些有向无环图表征揭示了每种偏倚的因果机制，并提示了恰当的校正策略。结论：有向无环图为理解诊断准确性研究中的偏倚结构提供了有价值的框架，应作为STARD和QUADAS-2等现有质量评估工具的补充。我们建议在研究设计阶段纳入有向无环图以前瞻性识别潜在偏倚，并在报告阶段使用以提升透明度。有向无环图的构建需要跨学科合作，并应在不同因果结构下进行敏感性分析。