Speybroeck N, Williams C J, Lafia K B, Devleesschauwer B, Berkvens D
Institut de Recherche Santé et Société (IRSS), Université catholique de Louvain, Brussels, Belgium.
Med Vet Entomol. 2012 Dec;26(4):361-71. doi: 10.1111/j.1365-2915.2012.01015.x. Epub 2012 Apr 8.
Several statistical methods have been proposed for estimating the infection prevalence based on pooled samples, but these methods generally presume the application of perfect diagnostic tests, which in practice do not exist. To optimize prevalence estimation based on pooled samples, currently available and new statistical models were described and compared. Three groups were tested: (a) Frequentist models, (b) Monte Carlo Markov-Chain (MCMC) Bayesian models, and (c) Exact Bayesian Computation (EBC) models. Simulated data allowed the comparison of the models, including testing the performance under complex situations such as imperfect tests with a sensitivity varying according to the pool weight. In addition, all models were applied to data derived from the literature, to demonstrate the influence of the model on real-prevalence estimates. All models were implemented in the freely available R and OpenBUGS software and are presented in Appendix S1. Bayesian models can flexibly take into account the imperfect sensitivity and specificity of the diagnostic test (as well as the influence of pool-related or external variables) and are therefore the method of choice for calculating population prevalence based on pooled samples. However, when using such complex models, very precise information on test characteristics is needed, which may in general not be available.
已经提出了几种基于混合样本估计感染流行率的统计方法，但这些方法通常假定应用完美的诊断测试，而实际上并不存在这样的测试。为了优化基于混合样本的流行率估计，对当前可用的和新的统计模型进行了描述和比较。测试了三组模型：(a) 频率学派模型，(b) 蒙特卡洛马尔可夫链（MCMC）贝叶斯模型，以及 (c) 精确贝叶斯计算（EBC）模型。模拟数据使得能够对这些模型进行比较，包括测试在复杂情况下的性能，例如具有根据混合权重而变化的灵敏度的不完美测试。此外，所有模型都应用于从文献中获得的数据，以证明模型对实际流行率估计的影响。所有模型都在免费可用的R和OpenBUGS软件中实现，并在附录S1中给出。贝叶斯模型可以灵活地考虑诊断测试的不完美灵敏度和特异性（以及与混合相关或外部变量的影响），因此是基于混合样本计算人群流行率的首选方法。然而，在使用这种复杂模型时，需要非常精确的测试特征信息，而一般来说可能无法获得这些信息。
