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

TL;DR

This paper introduces the use of directed acyclic graphs (DAGs) to systematically illustrate and understand common biases in diagnostic test accuracy studies, enhancing bias identification and correction strategies.

Contribution

It develops DAG structures specific to DTA biases, demonstrating their parallels with etiological biases and advocating for their integration in study design and reporting.

Findings

DAGs reveal causal mechanisms of five major biases in DTA studies.

Structural parallels between DTA and etiological biases are established.

DAGs can guide bias correction and improve study transparency.

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.