arXiv subjects

Nandini Dendukuri

Publications and source records attributed to Nandini Dendukuri.

3 recordsLinked to original sources

arXiv2026

Improving interpretation of latent class models for diagnostic tests by recognizing their measurands via directed acyclic graphs (DAGs)

Summary: In the absence of a perfect diagnostic test for a target condition, multiple imperfect tests may be used to arrive at a clinical diagnosis. Latent class analysis can be used to model such data with the objective of estimating test accuracy and target condition prevalence. Such models typically assume two latent classes - target condition positive and target condition negative. However, as we will illustrate in this manuscript, this would be an oversimplification if the different tests do not share the target condition as their measurand. We show how a Directed Acyclic Graph (DAG) can be used to illustrate the relationships between the relevant variables - the observed imperfect test results, their latent measurands, the latent target condition of interest and observed covariates - revealing any conditional dependence relations. The DAG helps determine the number of latent classes, underlying the observed data, and their labels. We show how the likelihood function changes due to incorporating the measurand of each test. We study the impact on identifiability of the model. Using simulation studies we show how ignoring the measurand of an imperfect test, when it is distinct from the target condition, can lead to biased estimates of test accuracy and prevalence. We illustrate the value of the proposed approach by re-analyzing two datasets used in previously published latent class analyses of tests for pediatric tuberculosis and leptospirosis.

stat.ME↗

arXiv2026

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

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.

stat.ME↗

arXiv2025

Examining the Association between Estimated Prevalence and Diagnostic Test Accuracy using Directed Acyclic Graphs

There have been reports of correlation between estimates of prevalence and test accuracy across studies included in diagnostic meta-analyses. It has been hypothesized that this unexpected association arises because of certain biases commonly found in diagnostic accuracy studies. A theoretical explanation has not been studied systematically. In this work, we introduce directed acyclic graphs to illustrate common structures of bias in diagnostic test accuracy studies and to define the resulting data-generating mechanism behind a diagnostic meta-analysis. Using simulation studies, we examine how these common biases can produce a correlation between estimates of prevalence and index test accuracy and what factors influence its magnitude and direction. We found that an association arises either in the absence of a perfect reference test or in the presence of a covariate that simultaneously causes spectrum effect and is associated with the prevalence (confounding). We also show that the association between prevalence and accuracy can be removed by appropriate statistical methods. In the risk of bias evaluation in diagnostic meta-analyses, an observed association between estimates of prevalence and accuracy should be explored to understand its source and to adjust for latent or observed variables if possible.

stat.ME↗