Estimating disease prevalence in the absence of a gold standard
Estimating disease prevalence in the absence of a gold standard
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DOI：
10.1002/sim.1178
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发表时间：
2002-09-30
影响因子：
2
通讯作者：
Craig, BA
中科院分区：
文献类型：
--
作者：
Black, MA;Craig, BA
在估计疾病患病率时，从有条件的诊断测试中获得数据并不少见。在这种情况下，如果没有一项测试被认为是黄金标准，就很难估计患病率。在这篇文章中，我们开发了一种基于两个诊断测试的结果来估计疾病流行率的贝叶斯方法，允许测试是有条件的依赖的，但不是以任何特定的依赖结构为条件的。这涉及构建具有各种形式的条件依赖的四个模型，并使用贝叶斯模型平均，由可逆跳跃MCMC实现，以获得流行率的总体估计。这一方法是通过一项关于类圆线虫感染流行率的研究来证明的。版权所有(C)2002 John Wiley Sons，Ltd.
When estimating disease prevalence, it is not uncommon to have data from conditionally dependent diagnostic tests. In such a situation, the estimation of prevalence is difficult if none of the tests is considered to be a gold standard. In this paper we develop a Bayesian approach to estimating disease prevalence based on the results of two diagnostic tests, allowing for the possibility that the tests are conditionally dependent, but not conditioning on any particular dependence structure. This involves the construction of four models with various forms of conditional dependence and uses Bayesian model averaging, enabled by reversible jump MCMC, to obtain an overall estimate of the prevalence. This methodology is demonstrated using a study on the prevalence of Strongyloides infection. Copyright (C) 2002 John Wiley Sons, Ltd.