PDF(Pubmed)
Abstract：
BACKGROUND: Causal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis.
METHODS: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation \"cross-world\" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH).
RESULTS: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions.
CONCLUSIONS: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.
METHODS: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation \"cross-world\" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH).
RESULTS: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions.
CONCLUSIONS: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.
摘要：
背景：因果中介分析在检查因果效应和因果机制中起着至关重要的作用。然而，有限的工作考虑了在因果调解分析中使用抽样权重。在这项研究中,我们比较了将抽样权重纳入因果中介分析的不同策略.
方法：我们进行了一项模拟研究，以评估4种不同的抽样加权策略-1）不使用抽样权重，2)将抽样权重纳入调解“跨世界”权重，3)在估计结果模型时使用抽样权重，和4)在两个阶段都使用抽样权重。我们生成了8个模拟人口情景，包括暴露(A)，结果(Y)，调解员(M)，和六个协变量(C)，所有这些都是二进制的。生成数据，使得A给定C的真实模型和A给定M和C的真实模型都是logit模型。我们用4种不同的抽样方法对这8种人口情景进行了交叉，获得了32种总的模拟条件。对于每个模拟条件，在计算基于样本的总体估计值时，我们评估了4种抽样加权策略的性能，直接，和间接影响。我们还使用来自全国药物使用和健康调查（NSDUH）的数据将四种抽样加权策略应用于案例研究。
结果：在所检查的大多数模拟条件下，在两个阶段（中介权重估计和结果模型）中使用采样权重的偏差最小。仅在一个阶段中使用采样权重会导致多个模拟条件的更大偏差。
结论：在关于总体数据结构和抽样方法的各种条件下，在两个阶段中使用抽样权重是减少因果中介分析中偏差的有效方法。
方法：我们进行了一项模拟研究，以评估4种不同的抽样加权策略-1）不使用抽样权重，2)将抽样权重纳入调解“跨世界”权重，3)在估计结果模型时使用抽样权重，和4)在两个阶段都使用抽样权重。我们生成了8个模拟人口情景，包括暴露(A)，结果(Y)，调解员(M)，和六个协变量(C)，所有这些都是二进制的。生成数据，使得A给定C的真实模型和A给定M和C的真实模型都是logit模型。我们用4种不同的抽样方法对这8种人口情景进行了交叉，获得了32种总的模拟条件。对于每个模拟条件，在计算基于样本的总体估计值时，我们评估了4种抽样加权策略的性能，直接，和间接影响。我们还使用来自全国药物使用和健康调查（NSDUH）的数据将四种抽样加权策略应用于案例研究。
结果：在所检查的大多数模拟条件下，在两个阶段（中介权重估计和结果模型）中使用采样权重的偏差最小。仅在一个阶段中使用采样权重会导致多个模拟条件的更大偏差。
结论：在关于总体数据结构和抽样方法的各种条件下，在两个阶段中使用抽样权重是减少因果中介分析中偏差的有效方法。
