Division of Biostatistics - University of Miami, Don Soffer Clinical Research Center, 1120 NW 14th St, Miami, FL 33136, United States.
J Theor Biol. 2021 Mar 7;512:110556. doi: 10.1016/j.jtbi.2020.110556. Epub 2020 Dec 30.
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.
新冠病毒检测已成为估计流行率的标准方法，这有助于公共卫生决策来控制和减轻疾病的传播。所使用的抽样设计往往存在偏差，因为它们不能反映真实的基础人群。例如，有强烈症状的个体比没有症状的个体更有可能接受检测。这导致流行率的估计值出现偏差（过高）。通常情况下，不可能进行事后抽样校正。在这里，我们提出了一种简单的偏差校正方法，该方法源自荟萃分析研究中对发表偏倚的校正，并进行了适当的调整。该方法足够通用，可以进行各种定制，使其在实践中更有用。通过已经收集到的信息，很容易实现实施。通过模拟和两个真实数据集，我们表明偏差校正可以显著减少估计误差。
