期刊文章%@ 2369-2960 %I JMIR出版物%V 8 %N 卡塔尔世界杯8强波胆分析9 %P 37887 %T在监测数据中对人口水平摘要的非随机遗漏的影响:仿真研究% Weiss,保罗·塞缪尔·沃勒%,兰斯阿林% +罗林斯公共卫生学院,埃默里大学,1518克利夫顿Rd NE, 308房间,亚特兰大,乔治亚州,30322 - 4201,美国,1 404 712 9641,paul.weiss@emory.edu % K监视% K估计% K缺失数据% K全民估计% K卫生政策% K公共卫生政策% K估计% K数据% K决策% K偏见% K反应率% D原始论文7 9.9.2022 % 9 2022% % J JMIR公共卫生Surveill % G英语% X背景:监测数据是指导政策和人力和资本资源分配的重要公共卫生资源。这些数据通常由基于非随机样本设计的大量信息组成。基于这些数据的人口估计可能会受到基础样本分布与真正感兴趣的人口相比的影响。在本研究中，我们模拟了一个感兴趣的人群，并允许应答率以非随机的方式变化，以说明和衡量这对一个重要公共卫生政策结果的基于人群的估计的影响。目的:本研究旨在阐明非随机缺失对基于人口的调查样本估计的影响。方法:我们模拟了一组受访者，他们回答了一个关于他们对政府人员接种疫苗的社区政策的满意度的调查问题。我们允许一般满意者和不满意者之间的回复率不同，并考虑了共同努力的影响，以控制潜在的偏差，如抽样权重、样本量膨胀和随机确定缺失的假设检验。我们通过均方误差和抽样可变性来比较这些条件，以表征在这些不同方法下产生的估计偏差。 Results: Sample estimates present clear and quantifiable bias, even in the most favorable response profile. On a 5-point Likert scale, nonrandom missingness resulted in errors averaging to almost a full point away from the truth. Efforts to mitigate bias through sample size inflation and sampling weights have negligible effects on the overall results. Additionally, hypothesis testing for departures from random missingness rarely detect the nonrandom missingness across the widest range of response profiles considered. Conclusions: Our results suggest that assuming surveillance data are missing at random during analysis could provide estimates that are widely different from what we might see in the whole population. Policy decisions based on such potentially biased estimates could be devastating in terms of community disengagement and health disparities. Alternative approaches to analysis that move away from broad generalization of a mismeasured population at risk are necessary to identify the marginalized groups, where overall response may be very different from those observed in measured respondents. %M 36083618 %R 10.2196/37887 %U https://publichealth.www.mybigtv.com/2022/9/e37887 %U https://doi.org/10.2196/37887 %U http://www.ncbi.nlm.nih.gov/pubmed/36083618

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