使用倾向性评分加权进行调节分析教程：性少数成年人吸烟差异的应用
A tutorial for using propensity score weighting for moderation analysis: an application to smoking disparities among sexual minority adults
AI总结：
本教程提供使用倾向性评分加权估计调节效应的分步指导和代码，通过案例研究证明该方法能改善协变量不平衡导致的偏差，从而更准确地估计调节效应。
摘要：
目标：提供使用倾向性评分（PS）加权估计调节效应的分步指导以及STATA和R代码。研究设计：教程说明了使用观察数据估计和检验调节效应的关键步骤。步骤包括：（1）检查调节变量各水平内治疗组之间的协变量重叠情况，（2）估计PS权重，（3）评估PS权重是否改善了协变量平衡，（4）估计调节处理效应，以及（5）评估结果对未观测混杂因素的敏感性。我们的示例案例研究使用了2019年国家药物使用与健康调查中41,832名成年人的数据，以检验性别是否调节性少数身份（例如，女同性恋、男同性恋或双性恋[LGB]身份）与成人吸烟流行率之间的关联。结果：对于我们的案例研究，协变量重叠没有引起关注的问题，我们能够成功地在调节变量的每个水平内估计PS权重。此外，平衡标准表明PS权重成功实现了两个调节变量组的协变量平衡。PS加权结果显示案例研究中存在显著的调节证据，敏感性分析表明结果在调节变量的一个水平上高度稳健，但在另一个水平上则不然。结论：在进行调节分析时，调节变量各水平间的协变量不平衡可能导致有偏估计。如本教程所示，在调节变量的每个水平内进行PS加权可以通过最小化调节亚组内不平衡带来的偏差来改善估计的调节效应。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
