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

2026-07-15

AI Summary

Traditional latent class models often erroneously assume that all diagnostic tests measure the same underlying latent variable, disregarding that different tests may target distinct measurands, thereby inducing bias in prevalence and accuracy estimates. This study systematically introduces directed acyclic graphs (DAGs) to explicitly represent the conditional dependencies among test results, measurands, the target disease status, and covariates. Leveraging this graphical framework, the authors reformulate the structure and likelihood function of latent class models, redefine the number and labeling of latent classes, and consequently enhance model identifiability and parameter estimation accuracy. Monte Carlo simulations and analyses of real-world data on pediatric tuberculosis and leptospirosis demonstrate that the proposed approach effectively corrects bias arising from ignoring differences in measurands, substantially improving the interpretability and reliability of latent class models in clinical settings.

Abstract

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.

Problem

• latent class models
• diagnostic tests
• measurands
• test accuracy
• prevalence estimation

Innovation

• latent class model
• measurand
• directed acyclic graph
• diagnostic test accuracy
• conditional dependence