Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data.
Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data.
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DOI：
10.1038/srep11861
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发表时间：
2015-07-07
影响因子：
4.6
通讯作者：
Bishop SC
中科院分区：
文献类型：
--
作者：
Bermingham ML;Handel IG;Glass EJ;Woolliams JA;de Clare Bronsvoort BM;McBride SH;Skuce RA;Allen AR;McDowell SW;Bishop SC
诊断试验的灵敏度和特异度是概率估计，对疾病控制、管理和遗传研究具有深远的影响。在没有“黄金标准”测试的情况下，传统的贝叶斯潜伏期模型可用于通过对同一组个体进行的两个或多个测试的比较来评估诊断测试的准确性。这项研究的目的是利用疾病监测数据，将这些模型扩展到估计诊断测试参数和真正的队列特定流行率。传统的惠-沃尔特潜伏期分类方法被扩展到允许在这类数据中看到的特征，包括(I)未记录的数据(即，第二次测试的数据仅在样本总体的一个子集上可用)和(Ii)队列特有的敏感性和特异性。该模型在有和没有测试之间的条件依赖模型的情况下被应用。通过对北爱尔兰和爱尔兰共和国的牛结核病监测数据的应用，证明了扩展模型的实用性。模拟与再抽样技术相结合，表明扩展模型具有很好的预测能力，能够从监测数据中估计诊断参数和真实的羊群水平患病率。我们的方法可以帮助解释疾病监测数据，结果可能会改进疾病控制战略。
Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of ‘gold standard’ tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.
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