Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
BMC Med Res Methodol. 2023 Mar 10;23(1):58. doi: 10.1186/s12874-023-01873-0.
Latent class models are increasingly used to estimate the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and are commonly fitted using Bayesian methods. These models allow us to account for 'conditional dependence' between two or more diagnostic tests, meaning that the results from tests are correlated even after conditioning on the person's true disease status. The challenge is that it is not always clear to researchers whether conditional dependence exists between tests and whether it exists in all or just some latent classes. Despite the increasingly widespread use of latent class models to estimate diagnostic test accuracy, the impact of the conditional dependence structure chosen on the estimates of sensitivity and specificity remains poorly investigated.
A simulation study and a reanalysis of a published case study are used to highlight the impact of the conditional dependence structure chosen on estimates of sensitivity and specificity. We describe and implement three latent class random-effect models with differing conditional dependence structures, as well as a conditional independence model and a model that assumes perfect test accuracy. We assess the bias and coverage of each model in estimating sensitivity and specificity across different data generating mechanisms.
The findings highlight that assuming conditional independence between tests within a latent class, where conditional dependence exists, results in biased estimates of sensitivity and specificity and poor coverage. The simulations also reiterate the substantial bias in estimates of sensitivity and specificity when incorrectly assuming a reference test is perfect. The motivating example of tests for Melioidosis highlights these biases in practice with important differences found in estimated test accuracy under different model choices.
We have illustrated that misspecification of the conditional dependence structure leads to biased estimates of sensitivity and specificity when there is a correlation between tests. Due to the minimal loss in precision seen by using a more general model, we recommend accounting for conditional dependence even if researchers are unsure of its presence or it is only expected at minimal levels.
在缺乏金标准的情况下，潜类别模型越来越多地用于估计诊断测试的灵敏度和特异性，并且通常使用贝叶斯方法进行拟合。这些模型允许我们解释两个或多个诊断测试之间的“条件依赖”，这意味着即使在对个体真实疾病状态进行条件化后，测试结果也具有相关性。挑战在于，研究人员并不总是清楚测试之间是否存在条件依赖，以及这种依赖是否存在于所有或某些潜在类别中。尽管越来越广泛地使用潜类别模型来估计诊断测试的准确性，但选择的条件依赖结构对灵敏度和特异性估计的影响仍未得到充分研究。
使用模拟研究和对已发表病例研究的重新分析来突出选择的条件依赖结构对灵敏度和特异性估计的影响。我们描述并实现了三种具有不同条件依赖结构的潜类别随机效应模型，以及一种条件独立模型和一种假设完美测试准确性的模型。我们评估了每种模型在不同数据生成机制下估计灵敏度和特异性的偏差和覆盖度。
研究结果强调，在存在条件依赖的情况下，假设潜在类别中测试之间的条件独立会导致灵敏度和特异性的估计存在偏差和覆盖不足。模拟还重申了当错误地假设参考测试是完美的时，灵敏度和特异性估计存在严重偏差。针对 Melioidosis 测试的示例突出说明了这些实践中的偏差，并在不同模型选择下发现了估计测试准确性的重要差异。
我们已经说明了，在测试之间存在相关性时，条件依赖结构的错误指定会导致灵敏度和特异性的偏差估计。由于使用更一般的模型会导致最小的精度损失，因此我们建议即使研究人员不确定其存在或仅预期存在最低水平，也要考虑条件依赖。
