Many epidemiologic studies forgo probability sampling and turn to nonprobability volunteer-based samples because of cost, response burden, and invasiveness of biological samples. However, finite population inference is difficult to make from the nonprobability samples due to the lack of population representativeness. Aiming for making inferences at the population level using nonprobability samples, various inverse propensity score weighting (IPSW) methods have been studied with the propensity defined by the participation rate of population units in the nonprobability sample. In this paper, we propose an adjusted logistic propensity weighting (ALP) method to estimate the participation rates for nonprobability sample units. Compared to existing IPSW methods, the proposed ALP method is easy to implement by ready-to-use software while producing approximately unbiased estimators for population quantities regardless of the nonprobability sample rate. The efficiency of the ALP estimator can be further improved by scaling the survey sample weights in propensity estimation. Taylor linearization variance estimators are proposed for ALP estimators of finite population means that account for all sources of variability. The proposed ALP methods are evaluated numerically via simulation studies and empirically using the na\"ive unweighted National Health and Nutrition Examination Survey III sample, while taking the 1997 National Health Interview Survey as the reference, to estimate the 15-year mortality rates.
翻译:由于生物样品的成本、反应负担和侵入性,许多流行病学研究都认为概率抽样是可能的,转而采用非概率的志愿抽样,但由于缺乏人口代表性,很难从非概率抽样中作出有限的人口推断。为了利用非概率抽样在人口一级作出推断,已经研究过各种反常性评分加权法,研究的是人口单位参与非概率抽样比率界定的倾向性。在本文件中,我们建议采用调整的后勤适度加权法来估计非概率抽样单位的参与率。与现有的IPSW方法相比,拟议的ALP方法很容易使用现成的软件来实施,而不论非概率抽样率如何,对人口数量作出基本公正的估计。ALP估计法的效率可以通过扩大宣传性估计中的调查抽样比重来进一步提高。Taylor线性比重(ALP)计算法用来估计非概率抽样单位的参与率。 与现有的IPSW方法相比,拟议的ALP方法很容易用现用的软件对人口数量进行推估,而不论非概率抽样率率率率 3。 ALP估计方法的计算出1997年国家抽样调查方法是采用固定人口调查的方法。