Wang Zhuoyu, Dendukuri Nandini, Zar Heather J, Joseph Lawrence
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, H3A 1A2, Canada.
Division of Clinical Epidemiology, McGill University Health Center, Montreal, Quebec, H3A 1A1, Canada.
Stat Med. 2017 Dec 30;36(30):4843-4859. doi: 10.1002/sim.7449. Epub 2017 Sep 5.
When multiple imperfect dichotomous diagnostic tests are applied to an individual, it is possible that some or all of their results remain dependent even after conditioning on the true disease status. The estimates could be biased if this conditional dependence is ignored when using the test results to infer about the prevalence of a disease or the accuracies of the diagnostic tests. However, statistical methods correcting for this bias by modelling higher-order conditional dependence terms between multiple diagnostic tests are not well addressed in the literature. This paper extends a Bayesian fixed effects model for 2 diagnostic tests with pairwise correlation to cases with 3 or more diagnostic tests with higher order correlations. Simulation results show that the proposed fixed effects model works well both in the case when the tests are highly correlated and in the case when the tests are truly conditionally independent, provided adequate external information is available in the form of fixed constraints or prior distributions. A data set on the diagnosis of childhood pulmonary tuberculosis is used to illustrate the proposed model.
当对个体应用多个不完善的二分诊断测试时，即使在以真实疾病状态为条件之后，它们的某些或所有结果仍有可能保持依赖关系。如果在使用测试结果推断疾病患病率或诊断测试准确性时忽略这种条件依赖性，估计值可能会有偏差。然而，通过对多个诊断测试之间的高阶条件依赖项进行建模来校正这种偏差的统计方法在文献中并未得到很好的阐述。本文将具有成对相关性的两个诊断测试的贝叶斯固定效应模型扩展到具有高阶相关性的三个或更多诊断测试的情况。模拟结果表明，所提出的固定效应模型在测试高度相关的情况下以及测试真正条件独立的情况下都能很好地工作，前提是有足够的外部信息以固定约束或先验分布的形式提供。使用一个关于儿童肺结核诊断的数据集来说明所提出的模型。
