College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China; Centre for Applied One Health Research and Policy Advice, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region of China.
Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.
Prev Vet Med. 2022 Apr;201:105596. doi: 10.1016/j.prevetmed.2022.105596. Epub 2022 Feb 17.
Bayesian finite mixture models, frequently referred to as Bayesian latent class models have become increasingly common for diagnostic test data in the absence of a gold standard test. Most Bayesian analyses in the veterinary literature have dealt with a dichotomised diagnostic outcome. The use of Bayesian finite mixture models for continuous test outcomes, such as sample to positive (S/P) ratios produced by an ELISA, is much less common, despite continuous models taking advantage of all of the information captured in the test outcome. This paper revisits the idea of the Bayesian finite mixture model and provides a practical guide for researchers who would like to use this approach for modelling continuous diagnostic outcomes as it preserves all information from the observed data. Synthetic datasets and a dataset from literature were analysed to illustrate that a mixture model with continuous diagnostic outcomes can be used to estimate true prevalence and to evaluate test sensitivity and specificity. In addition, directly modelling the continuous test outcomes rather than dichotomising them, means that optimal cut-offs can be defined based on the test purpose rather than being determined before testing. Moreover, as animals with higher scores are more likely to be infected, using continuous data allows test interpretation to be made at the individual animal level. In contrast, dichotomization treats all animals above a cut-off as having the same infection risk. This study demonstrates that dichotomisation is not a 'must' when using Bayesian latent class analysis for diagnostic test data, and suggests that latent class analysis using continuous test outcomes should be favoured when evaluating veterinary diagnostic tests producing continuous outcomes.
贝叶斯有限混合模型，通常被称为贝叶斯潜在类别模型，在缺乏金标准测试的情况下，已成为诊断测试数据中越来越常见的方法。兽医文献中的大多数贝叶斯分析都涉及二分类诊断结果。尽管连续模型利用了测试结果中捕获的所有信息，但对于连续测试结果（如 ELISA 产生的样本到阳性（S/P）比值），贝叶斯有限混合模型的使用却要少得多。本文重新探讨了贝叶斯有限混合模型的想法，并为希望使用这种方法对连续诊断结果进行建模的研究人员提供了实用指南，因为它保留了观测数据中的所有信息。分析了合成数据集和文献中的数据集，结果表明，具有连续诊断结果的混合模型可用于估计真实患病率，并评估测试的敏感性和特异性。此外，直接对连续测试结果进行建模，而不是将其二值化，意味着可以根据测试目的定义最佳截止值，而不是在测试之前确定。此外，由于得分较高的动物更有可能被感染，因此使用连续数据可以在个体动物层面上进行测试解释。相比之下，二值化将所有高于截止值的动物视为具有相同的感染风险。本研究表明，在使用贝叶斯潜在类别分析对诊断测试数据进行分析时，二值化并非“必须”，并且建议在评估产生连续结果的兽医诊断测试时，应优先使用基于连续测试结果的潜在类别分析。
