Using Directed Acyclic Graphs to Illustrate Common Biases in Diagnostic Test Accuracy Studies

2026-01-17

AI Summary

Diagnostic test accuracy studies are frequently compromised by multiple sources of bias—such as errors in the reference standard, partial verification bias, and spectrum effects—yet lack a systematic causal framework for their representation. This study introduces directed acyclic graphs (DAGs) into this domain for the first time, constructing causal structural models for five major types of bias and illustrating their mechanisms through real-world examples. The work not only clarifies the structural parallels between these biases and their counterparts in etiologic research but also provides a transparent tool to improve the design, analysis, and reporting of diagnostic studies. The authors advocate for the integration of DAGs into established quality assessment frameworks such as STARD and QUADAS-2 to enhance methodological rigor.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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.

Problem

• diagnostic test accuracy
• bias
• directed acyclic graphs
• reference standard error
• partial verification bias

Innovation

• Directed Acyclic Graphs
• Diagnostic Test Accuracy
• Bias Visualization
• Causal Inference
• Partial Verification Bias