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Improving Population Representativeness of the Inference from Non-Probability Sample Analysis

提高非概率样本分析推断的总体代表性

基本信息

批准号：
10046869
负责人：
Yan Li
金额：
$ 15.45万
依托单位：
UNIV OF MARYLAND, COLLEGE PARK
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

项目摘要

SUMMARY The critical role of population-representativeness for estimating disease incidence and prevalence has been widely accepted in epidemiologic studies. Improving population representativeness of nonprobability samples, such as samples of volunteers in epidemiologic studies or electronic health records, however, has received little attention by biostatisticians or epidemiologists. In this project, we propose two innovative “pseudoweight” construction methods: 1) two-step matching, and 2) calibration, under an adapted exchangeability assumption, for unbiased estimation of disease incidence and prevalence in the target population. The proposed methods, combined with machine learning methods for propensity score estimation, will achieve significant bias reduction, especially when selection into nonprobability samples is driven by complex relationships between the covariates. We will quantify the bias reduced by the proposed “pseudoweights”, numerically and empirically, on the estimation of disease incidence and prevalence in the target population. Monte Carlo simulation studies are designed under varying degrees of departure from the adapted exchangeability assumption to evaluate the bias of the proposed estimates. The robustness of the proposed estimators against varying sample sizes, number of clusters in survey, and complexities of the true propensity score modeling will be investigated in scenarios that differ by levels of non-linearity, non-additivity and correlations between covariates in the true propensity model. Using data from National Institutes of Health and the American Association of Retired Persons (NIH-AARP, a nonprobability cohort sample) data and the US National Health Interview Survey (NHIS, a probability survey sample), the proposed methods will be applied to estimate the prevalence of self-reported diseases and all-cause or all-cancer mortality rates for people aged 50-71 in the US. To test our methods, we will purposely select outcome variables that are available in both the NIH-AARP and the NHIS. Thus, the amount of bias in NIH-AARP estimates corrected by the proposed pseudoweights can be quantified in practice, assuming the weighted NHIS estimate is true. The proposed methods, although motivated by the volunteer-based epidemiological studies, have wide applications outside of epidemiology, such as electronic health records or web surveys. The results from this project can be used by epidemiologists and health policy makers to improve the understanding of the health-related characteristics in the general population. Computer software that implements the proposed methods will be made available for public use.

总结人口代表性在估计疾病发病率和流行率方面的关键作用一直是在流行病学研究中被广泛接受。提高非概率样本的总体代表性，例如流行病学研究中的志愿者样本或电子健康记录，引起生物统计学家或流行病学家的注意。在这个项目中，我们提出了两个创新的“伪八” 构造方法：1）两步匹配，和2）校准，在适应的交换假设下，对目标人群的发病率和患病率进行无偏估计。所提出的方法，结合机器学习方法进行倾向评分估计，将实现显著的偏差减少，特别是当选择到非概率样本是由复杂的关系，协变量我们将量化的偏见减少了拟议的“伪”，数字和根据经验，对目标人群的疾病发病率和流行率进行估计。蒙特卡罗模拟研究的目的是在不同程度的偏离适应交换假设以评估拟议估计数的偏倚。所提出的估计量对不同的样本量，调查中的聚类数，以及真实倾向评分模型的复杂性将在非线性、非相加性和相互关系程度不同的情况下进行调查真实倾向模型中的协变量。使用来自美国国立卫生研究院和美国美国退休人员协会（NIH-AARP，非概率队列样本）数据和美国国民健康访谈调查（NHIS，概率调查样本），所提出的方法将被应用于估计美国50-71岁人群自我报告疾病的患病率和全因或全癌症死亡率我们为了测试我们的方法，我们将特意选择在NIH-AARP中可用的结果变量，和NHIS。因此，建议的伪权重校正的NIH-AARP估计值的偏差量在实践中可以量化，假设加权NHIS估计是真实的。虽然所提出的方法受志愿者流行病学研究的启发，在流行病学之外有广泛的应用，例如电子健康记录或网络调查。该项目的结果可供流行病学家使用和卫生政策制定者，以提高对与健康有关的特点，人口实施拟议方法的计算机软件将提供给公众使用。