Using Capture-Recapture Methodology to Enhance Precision of Representative Sampling-Based Case Count Estimates.
Using Capture-Recapture Methodology to Enhance Precision of Representative Sampling-Based Case Count Estimates.
复制标题
使用捕获-重新捕获方法来提高基于代表性抽样的病例数估计的精度。
DOI：
10.1093/jssam/smab052
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发表时间：
2022
影响因子：
2.1
通讯作者：
Waller,LanceA
中科院分区：
文献类型：
--
作者：
Lyles,RobertH;Zhang,Yuzi;Ge,Lin;England,Cameron;Ward,Kevin;Lash,TimothyL;Waller,LanceA
应用连续原则性抽样设计进行诊断检测通常被视为监测传染病或慢性病流行率和病例数的理想方法。考虑到后勤以及及时和节约资源的必要性，监测工作一般可得益于创造性的设计和相应的统计方法，以提高基于抽样的估计数的精确度，并减少必要样本的规模。一种选择是用来自其他监测流的可用数据来增强分析，这些监测流在同一时间段内从感兴趣的人群中识别病例，但可能以高度非代表性的方式进行。我们考虑监测一个封闭的群体（例如，长期护理机构、患者登记处或社区），并鼓励使用捕获-再捕获方法来产生替代原则性抽样获得的病例总数估计值。在其实施过程中，即使是相对较小的简单或分层随机样本，也不仅提供了自己的有效估计，而且提供了唯一完全合理的方法来证明基于经典捕获-再捕获方法的第二次估计。我们最初提出加权平均的两个估计，以达到更高的精度比可以单独使用，然后显示如何一个新的单一的捕获-再捕获估计提供了一个统一的和更可取的选择。我们开发了一个基于狄利克雷多项式的可信区间的变体，以配合我们的混合设计为基础的情况下计数估计，以期提高覆盖性能。最后，我们通过模拟模拟急性传染病每日监测计划或年度监测计划来量化固定患者登记处内的新病例，从而证明了该方法的好处。
The application of serial principled sampling designs for diagnostic testing is often viewed as an ideal approach to monitoring prevalence and case counts of infectious or chronic diseases. Considering logistics and the need for timeliness and conservation of resources, surveillance efforts can generally benefit from creative designs and accompanying statistical methods to improve the precision of sampling-based estimates and reduce the size of the necessary sample. One option is to augment the analysis with available data from other surveillance streams that identify cases from the population of interest over the same timeframe, but may do so in a highly nonrepresentative manner. We consider monitoring a closed population (e.g., a long-term care facility, patient registry, or community), and encourage the use of capture–recapture methodology to produce an alternative case total estimate to the one obtained by principled sampling. With care in its implementation, even a relatively small simple or stratified random sample not only provides its own valid estimate, but provides the only fully defensible means of justifying a second estimate based on classical capture–recapture methods. We initially propose weighted averaging of the two estimators to achieve greater precision than can be obtained using either alone, and then show how a novel single capture–recapture estimator provides a unified and preferable alternative. We develop a variant on a Dirichlet-multinomial-based credible interval to accompany our hybrid design-based case count estimates, with a view toward improved coverage properties. Finally, we demonstrate the benefits of the approach through simulations designed to mimic an acute infectious disease daily monitoring program or an annual surveillance program to quantify new cases within a fixed patient registry.