Cowling D W, Gardner I A, Johnson W O
Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis 95616, USA.
Prev Vet Med. 1999 Apr 9;39(3):211-25. doi: 10.1016/s0167-5877(98)00131-7.
We review frequentist and Bayesian approaches for estimating animal-level disease prevalence using pooled samples obtained by simple random sampling. We determine the preferred approach for different prevalence scenarios and with varying knowledge about sensitivity and specificity values. When sensitivity and specificity are perfect or known, we can choose between the large-sample theory estimates and the one-to-one relationship exact estimates. When sensitivity and specificity are unknown, we must use large-sample theory estimates or Bayesian methodology (which gives exact estimates). However, when the large-sample theory produces a negative lower confidence limit, we must use one of the exact methods. We compare estimates from each approach using culture results from pools of 20 eggs from three flocks on a California ranch that were producing eggs that were contaminated with Salmonella enteritidis phage type 4.
我们回顾了使用简单随机抽样获得的混合样本估计动物层面疾病流行率的频率学派和贝叶斯方法。我们确定了针对不同流行情况以及对灵敏度和特异度值了解程度各异时的首选方法。当灵敏度和特异度完美或已知时，我们可以在大样本理论估计值和一对一关系的精确估计值之间进行选择。当灵敏度和特异度未知时，我们必须使用大样本理论估计值或贝叶斯方法（可给出精确估计值）。然而，当大样本理论产生负的下限置信限时，我们必须使用精确方法之一。我们使用来自加利福尼亚一个牧场的三个鸡群的20个鸡蛋池的培养结果，比较了每种方法的估计值，这些鸡群所产鸡蛋被4型肠炎沙门氏菌噬菌体污染。
