论文标题
COVID-19采样偏差的简单校正
A simple correction for COVID-19 sampling bias
论文作者
论文摘要
Covid-19测试已成为估计患病率的标准方法，然后有助于公共卫生决策，以控制和减轻疾病的传播。使用的抽样设计通常是偏见的，因为它们不能反映出真正的潜在人群。例如，与没有症状的人相比，患有强烈症状的个体更有可能接受测试。这导致了患病率的偏见（过高）。典型的采样后校正并非总是可能的。在这里，我们提出了一种简单的偏见校正方法，并根据元分析研究中出版偏见的校正得出和改编。该方法足以允许多种自定义使其在实践中更有用。使用已经收集的信息很容易完成实施。通过模拟和两个真实数据集，我们表明偏差校正可以在估计误差中显着减少。
COVID-19 testing has become a standard approach for estimating prevalence which then assist in public health decision making to contain and mitigate the spread of the disease. The sampling designs used are often biased in that they do not reflect the true underlying populations. For instance, individuals with strong symptoms are more likely to be tested than those with no symptoms. This results in biased estimates of prevalence (too high). Typical post-sampling corrections are not always possible. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. The methodology is general enough to allow a wide variety of customization making it more useful in practice. Implementation is easily done using already collected information. Via a simulation and two real datasets, we show that the bias corrections can provide dramatic reductions in estimation error.
