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用全球COVID-19趋势和影响调查 (CTIS) 的疫苗接种数据,探索各种估计的大数据悖论
Youqi Yang1, Walter Dempsey1, Peisong Han2
1Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Science advances
|May 31, 2024
概括
像COVID-19趋势和影响调查 (CTIS) 这样的大型非概率调查显示,与较小的概率调查相比,在估计COVID-19疫苗接种率时,错误率更高. 这突出了大数据研究中潜在的选择偏见问题.
科学领域:
- 流行病学 流行病学
- 调查方法 调查方法
- 生物统计学 生物统计学
背景情况:
- 在非概率样本中的选择偏差使准确的统计推理复杂化.
- 评估COVID-19疫苗接种率需要可靠的数据,这往往受到采样方法的挑战.
研究的目的:
- 使用国家基准数据,比较来自大型非概率调查 (CTIS) 和小概率调查 (CVoter) 的COVID-19疫苗接种率估计的准确性.
- 评估CTIS在估计疫苗接种时间和子组差异方面的准确性.
主要方法:
- 从CTIS和CVoter对第一剂COVID-19疫苗接种率的估计错误与COVID疫苗情报网络基准数据进行比较.
- 分析了CTIS的平均平方误差,用于接种疫苗的连续和子组差异.
主要成果:
- 对于整体疫苗接种率,CTIS显示的平均估计误差 (0.37) 比CVoter (0.14) 更大.
- 使用CTIS估计差异 (随着时间的推移或子组之间的差异) 与总比率相比,有效样本大小增加.
- 大数据悖论可能超越美国,并根据具体估计而有所不同.
结论:
- 像CTIS这样的非概率调查可能会出现显著的估计错误,这凸显了选择偏差的持续挑战.
- 虽然CTIS对整体率显示出更高的误差,但分析差异可能会提高其实用性,但建议谨慎.
- 调查结果表明,大数据悖论并不仅限于美国,其影响是估计和依赖的.
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