Using Directed Acyclic Graphs to Illustrate Common Biases in Diagnostic Test Accuracy Studies
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用有向无环图解释诊断准确性研究中的常见偏倚
- 研究问题
- 诊断准确性研究中的参考标准误差、部分验证、谱效应、混杂和条件独立性误设，如何通过因果图理解其形成机制？
- 方法与思路
- 作者为五类偏倚构建有向无环图，结合实际例子说明机制，并将其与病因研究中的误分类、选择及混杂等结构对应。
- 材料陈述的贡献
- 摘要称这些图能揭示诊断研究偏倚的因果结构并提示校正策略，建议在研究设计和报告中结合现有质量评价工具使用。
- 阅读关注点
- 应核对各图的变量定义、箭头依据及偏倚对应关系，并检查建议的校正策略是否需要额外数据或可识别性条件。
- 阅读边界
- 所据为 arXiv 预印本摘要，同行评审及期刊接收未核实；图示机制不直接保证可校正，具体策略及效果未说明。
原文摘要
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.