Choosing an Optimal Method for Causal Decomposition Analysis: A Better Practice for Identifying Contributing Factors to Health Disparities
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健康差异因果分解的估计方法选择
- 研究问题
- 对中介变量实施干预后，弱势与非弱势群体之间的健康差异会减少或剩余多少，如何依据变量类型与关系形式选择估计方法？
- 方法与思路
- 作者比较回归、加权和插补三类方法，提出支持分类及连续变量与非线性关系的扩展插补法，以及限制更强的简化回归法。
- 材料陈述的贡献
- 摘要称，两项扩展补充了现有方法的适用场景，并结合模拟提出选择建议；应用以美国调查数据分析心血管健康差异的中介因素。
- 阅读关注点
- 应核对差异减少与剩余的目标量定义、中介干预的识别条件，以及回归法的线性限制和不同方法比较时的模拟设定。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出完整识别假设，所称偏差与方差表现不能外推至所有场景。
原文摘要
Causal decomposition analysis provides a way to identify mediators that contribute to health disparities between marginalized and non-marginalized groups. In particular, the degree to which a disparity would be reduced or remain after intervening on a mediator is of interest. Yet, estimating disparity reduction and remaining might be challenging for many researchers, possibly because there is a lack of understanding of how each estimation method differs from other methods. In addition, there is no appropriate estimation method available for a certain setting (i.e., a regression-based approach with a categorical mediator). Therefore, we review the merits and limitations of the existing three estimation methods (i.e., regression, weighting, and imputation) and provide two new extensions that are useful in practical settings. A flexible new method uses an extended imputation approach to address a categorical and continuous mediator or outcome while incorporating any nonlinear relationships. A new regression method provides a simple estimator that performs well in terms of bias and variance but at the cost of assuming linearity, except for exposure and mediator interactions. Recommendations are given for choosing methods based on a review of different methods and simulation studies. We demonstrate the practice of choosing an optimal method by identifying mediators that reduce race and gender disparity in cardiovascular health, using data from the Midlife Development in the US study.