Mai Yujiao, Ha Trung, Soulakova Julia N
Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827.
Struct Equ Modeling. 2019;26(5):678-684. doi: 10.1080/10705511.2018.1559065. Epub 2019 Jan 22.
We propose a method suitable for analysis of cross-sectional studies with complex sampling and continuous variables. The method consists of R+4 steps, where R denotes the number of replications. In the first R+1 steps, the main and R replicate weights are used (one at a time) to estimate the product of coefficients for all mediation effects using a structural equation model. In step R+2, the standard errors of these estimates are computed via balanced repeated replications. In step R+3, the raw p-values corresponding to mediation effects are computed based on the generalized Sobel's tests. In the final step, R+4, the p-values are adjusted for multiplicity and statistical inferences regarding mediation effects are drawn. To illustrate the approach we examined significance of attitudes toward smoking bans as mediators in the association between smoking restrictions at work and nicotine dependence among male daily smokers.
我们提出了一种适用于分析具有复杂抽样和连续变量的横断面研究的方法。该方法由R + 4个步骤组成,其中R表示重复次数。在前R + 1个步骤中,使用主权重和R个重复权重(每次使用一个),通过结构方程模型估计所有中介效应的系数乘积。在步骤R + 2中,通过平衡重复复制计算这些估计值的标准误差。在步骤R + 3中,基于广义索贝尔检验计算与中介效应对应的原始p值。在最后一步,即步骤R + 4中,对p值进行多重性调整,并得出关于中介效应的统计推断。为了说明该方法,我们检验了在男性每日吸烟者中,对吸烟禁令的态度作为工作场所吸烟限制与尼古丁依赖之间关联的中介因素的显著性。