Johnson W O, Gastwirth J L, Pearson L M
Department of Statistics, University of California, Davis, CA 95616-8705, USA.
Am J Epidemiol. 2001 May 1;153(9):921-4. doi: 10.1093/aje/153.9.921.
The authors consider screening populations with two screening tests but where a definitive "gold standard" is not readily available. They discuss a recent article in which a Bayesian approach to this problem is developed based on data that are sampled from a single population. It was subsequently pointed out that such inferences will not necessarily be accurate in the sense that standard errors for parameters may not decrease as n increases. This problem will generally occur when the data are insufficient to estimate all of the parameters as is the case when screening a single population with two tests. If both tests are applied to units sampled from two populations, however, this particular difficulty disappears. In this article the authors further examine this issue and develop an approach based on sampling two populations that yields increasingly accurate inferences as the sample size increases.
作者考虑使用两种筛查测试对人群进行筛查，但此时难以获得明确的“金标准”。他们讨论了最近的一篇文章，其中基于从单一人群中抽样的数据，开发了一种针对此问题的贝叶斯方法。随后有人指出，从参数的标准误差可能不会随着n的增加而减小这个意义上来说，此类推断不一定准确。当数据不足以估计所有参数时，这个问题通常就会出现，比如用两种测试对单一人群进行筛查时的情况。然而，如果将两种测试都应用于从两个人群中抽取的样本，那么这个特殊的困难就会消失。在本文中，作者进一步研究了这个问题，并开发了一种基于对两个人群进行抽样的方法，该方法会随着样本量的增加而产生越来越准确的推断。
