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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.

摘要人口代表性在估计疾病发病率和流行率方面的关键作用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，然而，诸如流行病学研究中的志愿者样本或电子健康记录，收到的却很少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，2)校准，在适应的互换性假设下，对目标人群中的发病率和流行率进行公正的估计。所提出的方法，结合机器学习方法进行倾向性得分估计，将会达到显著的偏差约简，特别是当选择非概率样本是由协变量。我们将量化由拟议的“伪八位数”减少的偏差，从数字和从经验上讲，关于目标人群中疾病发病率和流行率的估计。蒙特卡洛模拟研究是在不同程度偏离适应的可交换性的情况下设计的对拟议估计数的偏差进行评估的假设。所提出的估计量的稳健性不同的样本大小、调查中的聚类数和真实倾向得分建模的复杂性将在不同程度的非线性、非可加性和相互关系不同的情况下进行研究真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退休人员协会(NIH-AARP，非概率队列样本)数据与美国国民健康访谈调查(NHIS，一种概率调查样本)，建议的方法将被应用于估计中国50-71岁人群自我报告疾病的患病率和全因或全癌死亡率我们。为了测试我们的方法，我们将有目的地选择在NIH-AARP中可用的结果变量和国家健康保险制度。因此，NIH-AARP估计中的偏差量由拟议的伪距校正可以在实践中量化，假设加权的NHIS估计为真。拟议的方法，尽管在基于志愿者的流行病学研究的推动下，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。这一项目的结果可供流行病学家使用和卫生政策制定者提高对健康相关特征的总体认识人口。实施拟议方法的计算机软件将提供给公众使用。