通过有向无环图（DAG）识别潜在测量值来改进诊断测试的潜在类别模型的解释
Improving interpretation of latent class models for diagnostic tests by recognizing their measurands via directed acyclic graphs (DAGs)
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中文总结 AI 辅助
研究针对诊断测试中潜在类别模型解释问题，利用有向无环图识别测量值，分析其对模型似然函数、可识别性的影响，通过模拟及重新分析数据集，揭示忽略不同测量值会致估计偏差，展示该方法价值。
中文摘要 AI 辅助
摘要：在缺乏针对目标疾病的完美诊断测试时，可能会使用多个不完美测试来进行临床诊断。潜在类别分析可用于对这类数据建模，以估计测试准确性和目标疾病患病率。此类模型通常假设两个潜在类别——目标疾病阳性和目标疾病阴性。然而，正如本文所示，如果不同测试的测量值不是目标疾病，这种假设就过于简化了。我们展示了如何使用有向无环图（DAG）来说明相关变量之间的关系——观察到的不完美测试结果、它们的潜在测量值、感兴趣的潜在目标疾病和观察到的协变量，揭示任何条件依赖关系。DAG有助于确定观察到的数据背后的潜在类别数量及其标签。我们展示了由于纳入每个测试的测量值，似然函数如何变化。我们研究了对模型可识别性的影响。通过模拟研究，我们表明当不完美测试的测量值与目标疾病不同时，忽略它会导致测试准确性和患病率的估计有偏差。我们通过重新分析先前发表的用于小儿结核病和钩端螺旋体病测试的潜在类别分析中的两个数据集，说明了所提出方法的价值。
英文摘要
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.
