Eiset Andreas Halgreen, Frydenberg Morten
Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark.
Department of Public Health, Aarhus University, Aarhus, Denmark.
Clin Epidemiol. 2022 Jul 7;14:835-847. doi: 10.2147/CLEP.S354733. eCollection 2022.
Propensity score-weighting for confounder control and multiple imputation to counter missing data are both widely used methods in epidemiological research. Combination of the two is not trivial and requires a number of decisions to produce valid inference. In this tutorial, we outline the assumptions underlying each of the methods, present our considerations in combining the two, discuss the methodological and practical implications of our choices and briefly point to alternatives. Throughout we apply the theory to a research project about post-traumatic stress disorder in Syrian refugees.
We detail how we used logistic regression-based propensity scores to produce "standardized mortality ratio"-weights and Substantive Model Compatible-Full Conditional Specification for multiple imputation of missing data to get the estimate of association. Finally, a percentile confidence interval was produced by bootstrapping.
A simple propensity score model with weight truncation at 1st and 99th percentile obtained acceptable balance on all covariates and was chosen as our model. Due to computational issues in the multiple imputation, two levels of one of the substantive model covariates and two levels of one of the auxiliary covariates were collapsed. This slightly modified propensity score model was the substantive model in the SMC-FCS multiple imputation, and regression models were set up for all partially observed covariates. We set the number of imputations to 10 and number of iterations to 40. We produced 999 bootstrap estimates to compute the 95-percentile confidence interval.
Combining propensity score-weighting and multiple imputation is not a trivial task. We present considerations necessary to do so, realizing it is demanding in terms of both workload and computational time; however, we do not consider the former a drawback: it makes some of the underlying assumptions explicit and the latter may be a nuisance that will diminish with faster computers and better implementations.
倾向评分加权用于混杂因素控制以及多重填补法用于处理缺失数据，这两种方法在流行病学研究中都被广泛使用。将这两种方法结合并非易事，需要做出一些决策才能得出有效的推断。在本教程中，我们概述了每种方法的基本假设，介绍了我们在将两者结合时的考虑因素，讨论了我们选择的方法在方法学和实际应用方面的影响，并简要指出了其他替代方法。在整个过程中，我们将理论应用于一个关于叙利亚难民创伤后应激障碍的研究项目。
我们详细说明了如何使用基于逻辑回归的倾向评分来生成“标准化死亡率比”权重，以及如何使用实质性模型兼容 - 完全条件设定法对缺失数据进行多重填补以获得关联估计。最后，通过自抽样法生成百分位数置信区间。
一个在第1和第99百分位数处进行权重截断的简单倾向评分模型在所有协变量上获得了可接受的平衡，并被选为我们的模型。由于多重填补中的计算问题，一个实质性模型协变量的两个水平和一个辅助协变量的两个水平被合并。这个略有修改的倾向评分模型成为SMC - FCS多重填补中的实质性模型，并为所有部分观测的协变量建立了回归模型。我们将填补次数设置为10次，迭代次数设置为40次。我们生成了999个自抽样估计值来计算第95百分位数置信区间。
结合倾向评分加权和多重填补并非易事。我们提出了这样做所需的考虑因素，意识到这在工作量和计算时间方面都要求很高；然而，我们并不认为前者是一个缺点：它使一些潜在假设变得明确，而后者可能是一个麻烦，但随着计算机速度更快和实现更好，这种麻烦将会减少。
