论文标题
来自随机样本和普查数据的患病率估计与参与偏见
Prevalence Estimation from Random Samples and Census Data with Participation Bias
论文作者
论文摘要
国家正式记录基于未知参与偏见的人口子集的医学测试的COVID-19案件数量。为了估算患病率，通常会丢弃官方信息，并取出小的随机调查样本。根据调查样本，我们得出了（最大的可能性和力矩方法）流行率估计量，该估计量还利用了官方信息，并且比正案例的简单样本比例更准确。换句话说，使用我们的估计量，可以通过大幅较小的调查样本获得相同水平的精度。由于医疗测试程序的敏感性和特异性，我们考虑了测量错误的可能性。提出的估计器和相关的置信区间在配套开源R软件包CAPE中实现。
Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population with unknown participation bias. For prevalence estimation, the official information is typically discarded and, instead, small random survey samples are taken. We derive (maximum likelihood and method of moment) prevalence estimators, based on a survey sample, that additionally utilize the official information, and that are substantially more accurate than the simple sample proportion of positive cases. Put differently, using our estimators, the same level of precision can be obtained with substantially smaller survey samples. We take into account the possibility of measurement errors due to the sensitivity and specificity of the medical testing procedure. The proposed estimators and associated confidence intervals are implemented in the companion open source R package cape.
