Bayesian analysis of tests with unknown specificity and sensitivity
Bayesian analysis of tests with unknown specificity and sensitivity
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DOI：
10.1111/rssc.12435
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发表时间：
2020-08-13
影响因子：
1.6
通讯作者：
Carpenter, Bob
中科院分区：
文献类型：
--
作者：
Gelman, Andrew;Carpenter, Bob
当检测一种罕见疾病时，患病率估计可能对检测的特异性和敏感性的不确定性高度敏感。贝叶斯推理是传播这些不确定性的一种自然方式，分层建模捕获了这些参数在实验中的变化。另一个问题是样本中的人不能代表一般人群。如果没有强有力的假设，统计调整不能纠正选择样本中的选择偏差，但多水平回归和后分层至少可以调整样本和总体之间的已知差异。我们用Stan中的代码演示了分层回归和后分层模型，并讨论了它们在最近有争议的斯坦福大学地区人群样本中SARS-CoV-2抗体研究中的应用。较宽的后验间隔使得不可能评估该研究关于未报告感染数量的定量主张。对于未来的研究，这里描述的方法应该有助于从对非代表性样本进行的不完善的测试中更准确地估计疾病的患病率。
When testing for a rare disease, prevalence estimates can be highly sensitive to uncertainty in the specificity and sensitivity of the test. Bayesian inference is a natural way to propagate these uncertainties, with hierarchical modelling capturing variation in these parameters across experiments. Another concern is the people in the sample not being representative of the general population. Statistical adjustment cannot without strong assumptions correct for selection bias in an opt-in sample, but multilevel regression and post-stratification can at least adjust for known differences between the sample and the population. We demonstrate hierarchical regression and post-stratification models with code in Stan and discuss their application to a controversial recent study of SARS-CoV-2 antibodies in a sample of people from the Stanford University area. Wide posterior intervals make it impossible to evaluate the quantitative claims of that study regarding the number of unreported infections. For future studies, the methods described here should facilitate more accurate estimates of disease prevalence from imperfect tests performed on non-representative samples.