A tutorial for using propensity score weighting for moderation analysis: an application to smoking disparities among sexual minority adults
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利用倾向得分加权进行调节效应分析：性少数成年人吸烟差异的应用教程
- 研究问题
- 在观测数据中分析调节效应时，如何处理各调节变量层内的协变量不平衡，并检验结论对未观测混杂的敏感性？
- 方法与思路
- 作者提供 Stata 与 R 教程，依次检查层内重叠、估计倾向得分权重、评估平衡、估计调节效应，并开展未观测混杂敏感性分析。
- 材料陈述的贡献
- 摘要报告，吸烟案例中加权后两组达到平衡并发现调节证据，但对未观测混杂的稳健性在两个调节变量层之间不同。
- 阅读关注点
- 应核对每层的目标效应、重叠与平衡判据、权重及标准误的处理，以及敏感性分析所允许的混杂机制和性少数身份的解释方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出效应大小、权重细节或敏感性参数，协变量平衡不能证明不存在未测混杂。
原文摘要
Objective. To provide step-by-step guidance and STATA and R code for using propensity score (PS) weighting to estimate moderation effects. Research Design. Tutorial illustrating the key steps for estimating and testing moderation using observational data. Steps include (1) examining covariate overlap across treatment groups within levels of the moderator, (2) estimating the PS weights, (3) evaluating whether PS weights improved covariate balance, (4) estimating moderated treatment effects, and (5) assessing sensitivity of findings to unobserved confounding. Our illustrative case study uses data from 41,832 adults from the 2019 National Survey on Drug Use and Health to examine if gender moderates the association between sexual minority status (e.g., lesbian, gay, or bisexual [LGB] identity) and adult smoking prevalence. Results. For our case study, there were no noted concerns about covariate overlap and we were able to successfully estimate the PS weights within each level of the moderator. Moreover, balance criteria indicated that PS weights successfully achieved covariate balance for both moderator groups. PS weighted results indicated there was significant evidence of moderation for the case study and sensitivity analyses demonstrated that results were highly robust for one level of the moderator but not the other. Conclusions. When conducting moderation analyses, covariate imbalances across levels of the moderator can cause biased estimates. As demonstrated in this tutorial, PS weighting within each level of the moderator can improve the estimated moderation effects by minimizing bias from imbalance within the moderator subgroups.