Variable Inclusion Strategies for Effective Quota Sampling and Propensity Modeling: An Application to SARS-COV-2 Infection Prevalence Estimation

IF 1.6 4区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Yan Li, M. Fay, Sally A. Hunsberger, B. Graubard
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引用次数: 0

Abstract

Public health policymakers must make crucial decisions rapidly during a pandemic. In such situations, accurate measurements from health surveys are essential. As a consequence of limited time and resource constraints, it may be infeasible to implement a probability-based sample that yields high response rates. An alternative approach is to select a quota sample from a large pool of volunteers, with the quota sample selection based on the census distributions of available—often demographic—variables, also known as quota variables. In practice, however, census data may only contain a subset of the required predictor variables. Thus, the realized quota sample can be adjusted by propensity score pseudoweighting using a “reference” probability-based survey that contains more predictor variables. Motivated by the SARS-CoV-2 serosurvey (a quota sample conducted in 2020 by the National Institutes of Health), we identify the condition under which the quota variables can be ignored in constructing the propensity model but still produce nearly unbiased estimation of population means. We conduct limited simulations to evaluate the bias and variance reduction properties of alternative weighting strategies for quota sample estimates under three propensity models that account for varying sets of predictors and degrees of correlation among the predictor sets and then apply our findings to the empirical data.
有效配额抽样和倾向建模的变量包含策略:在SARS-COV-2感染流行率估计中的应用
公共卫生政策制定者必须在大流行期间迅速作出关键决定。在这种情况下,来自健康调查的准确测量是必不可少的。由于有限的时间和资源限制,实现产生高响应率的基于概率的样本可能是不可行的。另一种方法是从大量志愿者中选择配额样本,配额样本的选择基于可用变量(通常是人口统计学变量)的普查分布,也称为配额变量。然而,在实践中,普查数据可能只包含所需预测变量的一个子集。因此,可以使用包含更多预测变量的“参考”基于概率的调查,通过倾向得分伪加权来调整实现的配额样本。受SARS-CoV-2血清调查(美国国立卫生研究院(National Institutes of Health)于2020年进行的配额样本)的启发,我们确定了在构建倾向模型时可以忽略配额变量但仍能对总体均值产生近乎无偏估计的条件。我们进行了有限的模拟,以评估三种倾向模型下配额样本估计的替代加权策略的偏差和方差减少特性,这些倾向模型考虑了不同的预测因子集和预测因子集之间的相关程度,然后将我们的发现应用于实证数据。
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来源期刊
CiteScore
4.30
自引率
9.50%
发文量
40
期刊介绍: The Journal of Survey Statistics and Methodology, sponsored by AAPOR and the American Statistical Association, began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data. It aims to be the flagship journal for research on survey statistics and methodology. Topics of interest include survey sample design, statistical inference, nonresponse, measurement error, the effects of modes of data collection, paradata and responsive survey design, combining data from multiple sources, record linkage, disclosure limitation, and other issues in survey statistics and methodology. The journal publishes both theoretical and applied papers, provided the theory is motivated by an important applied problem and the applied papers report on research that contributes generalizable knowledge to the field. Review papers are also welcomed. Papers on a broad range of surveys are encouraged, including (but not limited to) surveys concerning business, economics, marketing research, social science, environment, epidemiology, biostatistics and official statistics. The journal has three sections. The Survey Statistics section presents papers on innovative sampling procedures, imputation, weighting, measures of uncertainty, small area inference, new methods of analysis, and other statistical issues related to surveys. The Survey Methodology section presents papers that focus on methodological research, including methodological experiments, methods of data collection and use of paradata. The Applications section contains papers involving innovative applications of methods and providing practical contributions and guidance, and/or significant new findings.
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